AMD (Advanced Micro Devices) Aktie

Welcome back to this BofA Global Tech Conference. I'm Vivek Arya from BofA's Semiconductor, Semi-Cap Equipment Research team.

I'm really delighted to have the team from Advanced Micro Devices join us this morning: Jean Hu, Chief Financial Officer; and Matt Ramsay, Head of Investor Relations. And I'll go through my questions, but please feel free to raise your hand if you would like me to bring up something.

But really warm welcome to you, Jean and Matt. I really appreciate you joining us...

Yes. Thank you.

At the conference. And I guess, exciting times in AI and the semiconductor industry.

So maybe, Jean, as a start, just walk us through your state of the union, right? The growth has been very strong, right, since the start of the year. What has surprised you? Which are the areas that you think you are seeing kind of the best growth right now? And which are the areas which have been kind of pushed back because of this?

Yes. First, thank you for having us. Thank you all for joining us.

I think the biggest change during the last few months is really the rise and inflection of agentic AI. You can see continued momentum from training to inference, from AI adoption, experimentation, to more adoption at scale. So when you look at that, agentic AI, it's not about answering questions anymore. It's about orchestration, it's about database access and a lot of tool execution. And all of those require significant CPU performance. And what we are seeing is very significant and incremental demand for our CPU platforms. That has been really exciting.

At the same time, we also are seeing the economics of AI keeps changing, right? With the token generation going up so quickly, all the customers are really focusing on performance, TCO, and they're really trying to figure out how to use different computes to address different application and workloads. So we see demand for GPU going up, CPU and also a lot of other ASIC LPUs.

I think from AMD's perspective, you know our Q1 performance. We had record CPU performance. CPU business grew more than 50%. We guided Q2 CPU performance is going to go up year-over-year 70%. That has been really exciting. So all those things really benefit us because we have been investing in GPU, CPU, adaptive compute, to also address literally end-to-end applications, from data center to clients, to gaming. That feels like right now all the engines are really driving the business revenue growth very significantly.

Got it. So I think the growth in CPU has been kind of the biggest positive driver this year and we are kind of tracking this 2030 or end of the decade type of addressable opportunity, which I think you were the first to point out, right, would be $60 billion. Then said that doesn't sound that high enough, so they took it up to $100 billion. Then you took it up to $120 billion. And Jensen said, well, could be even more than that, right, up to $200 billion.

What do you think is the difference between all these forecasts? This is one of the most frequently asked questions, right, from the investment community. What is the right number? How does one kind of get to it? Is there like a simple unit and are we just all using different ASPs to get to a different number and everyone has the same unit growth?

You're asking a lot of questions here. But I think maybe let's take a step back. You're absolutely right, AMD was the first one. Literally last year, we started to talk about how AI drive the demand of CPUs. So last November, when we had our Financial Analyst Day, we actually outlined how we think about this opportunity under the TAM.

We basically said, okay, we are thinking 3 segments. One is the traditional general compute, which we are all very familiar with, has been driving the traditional enterprise application across the board. The second category is the head node, which is really the communication with the GPUs. You want speed to make sure you have the communication. We also outlined agentic AI last November, which is still very early, but our team already see the early signs of agentic AI. That has been the fastest-growing market in our view. That's when we said, okay, we believe the TAM is going to grow 18% CAGR to $60 billion in 2030.

Remember, the general purpose CPU used to be just a single-digit growth. So that market expansion, we saw it earlier, but we never estimated that it's going to be so fast. The pace and the speed of agentic AI adoption has been tremendous. So when you look at January, February, March, all the enterprise adoption started. You see Anthropic, OpenAI, their revenue has been going up very significantly. The demand for CPU continued to go up across all our customers. That's when we had our earnings call, we actually updated our market forecast to more than $120 billion.

Frankly, it's still very early, right, when you think about agentic AI adoption. What we are seeing is with agentic AI, it's quite complex. When you have millions of operations you have to execute, when you have a lot of agents concurrently working on different tasks, you actually need a very high core count CPUs.

We don't know what other companies, how they think about the TAM and the SAM. For us, we go through the bottom-up and the top-down approach. We know at this particular point, we feel pretty good about more than $120 billion. But it's evolving so fast and more and more complex, so you should expect for the agentic AI portion of the market, because you have so many diverse workloads, so complex, the ASP will continue to increase because, increasingly, you need very high core count CPUs, high-performance CPUs.

Matt, I don't know if you have other you can add here.

Sure. First off, thank you, Vivek, for hosting us and for you guys all coming. I think there's a couple of things, Vivek, that I would maybe add to Jean's comments. One is, I think for this whole audience, and us included, we were trying to figure out when was the primary driver of AI CapEx going to go from almost predominantly large model training and start the shift towards inference being a primary driver of CapEx.

And we're seeing that happen in real time. And I think the more powerful thing for our business and maybe the more transformative thing architecturally is during that transition, you're seeing chatbot inference become agentic inference. And what agentic inference does as a workload, right, you daisy-chain or can catenate all these automated agentic flows together. And between each inference task, there's a lot of CPU diverse work. So post-processing and data, figuring out what to tell the AI to do next, often based on the result of the prior inference. Where do I get the data from for the next step? Is it in a cloud? Is it in an ERP system, a payment system, a CRM system, wherever? Get all of that data, come back. Post-process the data from that step and feed it into the next agentic step for the AI.

And that workload is quite diverse. And that's what we're seeing driving the demand for Turin now in real time. And as the order book fills in for the 256 core 2-nanometer Venice parts that are going to launch in a couple of months and be the primary workhorse for next year, that's where we're seeing the order book really expand.

Got it. Is there a way to segment that market, whatever it happens to be, right, $120 billion, right, plus, in those 3 segments, Jean, that you described, kind of the traditional applications, the part of, let's call it, part of the AI cluster, and then agentic kind of stand-alone CPUs?

Yes. I think the traditional segment, it's very clear, I think 2025, different third party, I would say that's a $25 billion to $30 billion market opportunity. It will continue to grow. As Matt has mentioned, you actually have agents doing more work on the traditional database. So that traditional general purpose CPU, it's probably steady growth, but not as high.

The head nodes which is working with the GPUs, you can see that's when people talk about the ratios. Traditionally, it's 1 head node CPU manage your 8 GPUs. And then become 2 to -- yes, it's getting more and more. So over time that ratio is changing. So we do think the head node segment CPU will also grow very fast, much faster than the traditional general CPU.

But the most exciting portion of the market is actually agentic AI. So agentic AI, you actually -- you are seeing agentic AI server rack sit in between the traditional servers and the GPUs. And those racks are handling all those different workloads to really make sure all the agentic agents work. That market, we think, whatever, the $120 billion or $200 billion market opportunity, is the majority of that large market.

Over 50% of that market you think is that...

Agentic, yes. It is still very early right now. But if you just think about what Matt said, how complex, how dynamic those workloads will be. And we do see significant productivity improvement, especially software engineering side. Those are very complex workloads. You really need different core counts, you need really high performance, high core counts. You also need a simultaneous multi-thread, all those high-performance, you can manage millions of agents potentially.

Right. And the final question there is -- actually 2 last questions. One is x86 versus ARM. So if let's say that agentic opportunity does capture the dominant share of whatever that TAM number happens to be, what advantage, and if I dare ask, what disadvantage does x86 have versus ARM in capturing that?

Yes. I'll start. Matt can talk about the more technical side, is the way AMD has been thinking about it, there has never been, "Oh, x86 architecture or ARM architecture." It's more about how we provide the best TCO for customers. So for us, the performance is number one.

And secondly, what we have been trying to build is the breadth and the depth of platform portfolio. So if you look at our CPU platform, we have gone through 5 generations at the Turin, and the Venice is next generation. We had the breadth from the core count to 8, to 16 core count addressing enterprise applications to the Venice will have 256 core count.

So for us, we have all different configurations, design points to meet all the different compute to give them best TCOs. I don't know technically you can provide them.

Yes, Vivek, I think they're, as Jean talked about, the 3 different buckets, right? The traditional server workloads for enterprise, I think there is an affinity towards x86 because of the codebase. And many of those servers are deployed on-prem, some of them are deployed in third-party cloud. But it's basically the same workload depending on where you want to deploy it. And I think as these agents generate more traffic to all those traditional systems, then the x86 ecosystem is well positioned there.

Then you have the head nodes. I think that what you really need in head nodes is really high single-thread performance, really high bandwidth to -- I don't know, it's not a technical term, but feed the beast, of these really expensive accelerators that are in the box, right?

We will do our own head nodes for Helios. I think our large competitor in their NVL racks will basically do their own head nodes. And then there's a bunch of different XPU programs that are going to be launched by the industry. And I think when we have those conversations with customers, it's much more about what is the performance of your CPU such that I get the best utilization out of this really expensive XPU that I'm buying? It's not an Intel or AMD or x86 versus ARM conversation. It's a, what's the best server part?

And then when you talk about these agentic racks, they're -- it's really about how many concurrent agents can you run per rack or per megawatt of compute. And as Jean mentioned, really, really high-thread and core-count products, I think our road map is differentiated there.

And another thing that doesn't get mentioned but I think is important, is if you think about the dollars of CapEx that are going to come into the server market, the mission-critical nature of these systems, both head node and agentic racks, some of the RAS features and the security features of the x86 ecosystem, it's not an x86 architecture thing, it's just we and our x86 competitor have been put through the paces by every hyperscaler and most enterprises on all of these security features for a decade. And you might imagine, you get into these automated flows where those agents have access to mission-critical data, and you want to really have servers that have robust security features there. So we feel we get brought into essentially every RFQ that's in the industry for these servers, and I think we're positioned to win a very high percentage of them.

Got it. Now your x86 competitor has their own fab, right? They are adding up -- or they're planning to add a lot more capacity on the server CPU side. I guess this year, every CPU is being sold, right? But do you think that there is a scope for share shifts given that your competitor has incremental capacity to devote to this? Or do you think you will be adequately served by your foundry ecosystem?

Yes. We have been very pleased with our share gain trajectory. I think the last Q1 we just announced, we actually, from a value share perspective, we got to like 46%. When you think about it, is our success is really tied together with TSMC. From day 1, we have been using TSMC. We worked together with our chiplet design, with the packaging technology to come up with the best performance server platforms. That continues to benefit us going forward.

We have been planning for this ramp since last year. So if you look at the Q1 performance and the Q2 guidance, when we guide year-over-year 70% increase, and a lot of you actually know is right, the wafer started 6 or 9 months ago. So it is the early planning, how we work with TSMC to make sure we get support to continue to drive the ramp of the supply.

The demand is tremendous, supply is still tight. So I think for us, for this year, next year, every supply we can get, we can provide to our customers. We're going to continue to do that. It's the planning beyond the 2027 right now, for the longer term, I think we'll continue to work with TSMC and prioritize data center as how we drive the business growth going forward.

Vivek, one little thing that I would add to what Jean mentioned there is when we have conversations with the investor community on this particular topic, I think one thing that I've noticed is supply is tight. 3-nanometer is tight. There's a lot of other areas that are tight. And we're -- I think we're very well positioned with the relationships Lisa has personally with the executives in that space to get maybe more than our fair share of the incremental. But it is tight.

I think what I've noticed is maybe the investor scoping of what Lisa and the team initially asked for in '26 and '27 maybe didn't imagine how much growth we had already planned for in supply. So things are tight, but we've already asked for and already been sort of "granted" from TSMC, I think is a different -- I think we're having the right conversation there, but maybe not at the right starting point from what we were sort of allocated initially.

Got it. And then final one on the CPU side, on price. So you mentioned 50% plus growth in Q1 and then 70% plus. I think there was a sense that one could sustain this, right, for the rest of the year. How much of this, Jean, is pricing versus unit growth?

And then let me just add quickly Part B to that. As that agentic, right, that third segment becomes a bigger portion, do you think that can command much higher ASPs? When I talk with the ARM camp, they are talking about ASPs in the $3,000, $4,000, $5,000 range, which is well above the ASPs, right, that AMD is able to get. So just unit versus ASP and just the trend of ASP.

Yes. I think as you mentioned, both the more than 50% growth in Q1 and that we guided Q2 70% year-over-year, more than 70% year-over-year increase, 2/3 of that actually are unit growth. So majority of our revenue growth actually comes from unit expansion, versus ASP increase.

But to your point on the ASP going forward, especially with agentic AI, is a great point, is what we're seeing is we're at the early innings of agentic AI adoption. You can already see, with the software engineering part of agentic work -- autonomous agentic workflow, you really need very high performance, high core count CPUs. That tends to have a higher ASP.

So I think generation over generation, when we go to 2-nanometer with Venice and the future generation to handle complex workload to provide the performance, you tend to see the CPU price to continue to increase.

The other thing I would say is because we have really large platform, the CPU ASP is really determined by the mix and by the different configurations for different workloads. So it's not necessarily -- for enterprise, the per core ASP is very high, but because the core counts are relatively low, so ASP is relatively low. But the gross margin is great. So it's very nuanced from our business because of how big our platform is and how complex or different design point we are helping our customers.

Got it. We spent 2/3 of the time on CPU, so let's talk about GPU now. So you are at the start of launch of a major rack scale system to your portfolio. Maybe give us a status update of how you're feeling. When your competitor launched their first large scale-up product, it took a while, right, for the industry to get ready, liquid cooling, right, and all those things to get ready. So how do you think about your progress so far? How excited are you about the second half?

Yes. We are on track. We sampled MI450. We expect to be launching in second half or Q3 starting point, and Q4. As you know, we have been preparing for this launch for a long time. We acquired ZT Systems in 2024. The whole reason we're acquiring ZT Systems is to help us to add capabilities in system-level design. And we also have worked on the networking side.

So the whole team has been working to prepare this launch. We know it's very hard. It's very complex, very hard. But from an execution perspective, our team has been on track, meticulously preparing for all different aspect launch, not only technology, software side, but more importantly, the supply chain side, how you make sure the design redundancy with all the components, but also supplies, even very small components, that you have to make sure you have supplies. That has been this whole team's execution track record. We feel pretty good right now.

Yes. Vivek, I think we're anticipating, as Jean said, the -- we already have sampled and we have a number of customers that have full Helios racks in their own data center running their full production workload now and testing and doing all those things that you would expect us to be doing at this point in time.

I mean we learned -- the customers learned a lot, we learned a lot from some folks that blazed the trail, as you mentioned. And we're going to really focus on a concentrated set of ODMs as we launch for the first number of months until we get up to scale. And not like a few systems, but billions of dollars of scale. And then we'll be expanding the ODM ecosystem pretty significantly as we move through next year and get towards the MI500 series and whatnot.

So we feel good about where we are. The initial work that the customer base is doing in their own labs with sampled systems has gone very, very well. And so now it's about let's get -- we'll see a pretty significant jump in the fourth quarter of revenue and a fairly significant jump in Q1. And then we'll kind of go from there. So it is wood, so -- touch wood, everything is going exactly as we hoped it goes.

I mean when you're doing a thing like this, there's -- day to day, there's always something, right? But our job is to keep the duck calm on the top as we kick on the bottom, and get ready to ramp this thing starting in Q3.

Got it. When you've been asked, Jean, or Lisa has been asked, you've always mentioned the opportunity for multi-gigawatt scale deployments. I think we have heard about 2 of them, OpenAI and Meta. You think there is still the scope for us to be pleasantly surprised that more such announcements even for '27? Or do you think that just given how tight the industry is, that more incremental announcements could be further spaced out on that?

Yes. We talked about we're really pleased to lead our partnership, very long-term partnership, with both OpenAI and Meta. Last year, we established those relationships and we actually see the forecast from those customers are actually above our original plan for 2027.

I think one of the key milestones we're really focusing on this year is to continue to expand and deepen the customer adoption of MI450. We definitely expect there will be other mega, gigawatt customers. We have both across the board the hyperscale customers, the model companies and the even AI-native companies who we have been engaging, working with. I think it's important for us to continue to expand this customer base, and with the large scale deployment.

Got it. Makes sense. And then how do you expect the 2 customers you have announced to allocate market share? Because they have a number of supplier options. I know you have a somewhat unique engagement, right, with them through the granting of warrants and whatnot. But just if you set that aside, how is your visibility into getting, I think they're 2-gigawatt each, right, from those customers for next year?

Yes. Vivek, as you know, the lead time for MI450 is quite long. So we really have to start to plan and have the orders from customers for next year for the second half launch in the next year.

I think the supply chain overall is tight next year. But since we have been preparing to support our customers, we do want to ensure we get supplies for what they need. I think both are coming higher than our original plan, but we are continuing to increase the supply. Those are 2 of our most important anchor customers.

Of course, we also have a warrant, which we're going to be able to share the upside if we get more revenue. Our [ start ] price is higher, they will share upside too. That is absolutely the common interest for both companies that want to drive the adoption of MI450. We feel pretty good with the both of them. We think we can get the right supplies to support them.

Got it. Do you think having someone like a TSMC is almost adding a level of discipline, right, in the ecosystem? Because it's just harder to double count if everyone has to kind of go to the same source, right, to get -- I mean I know it sounds obvious, but do you see that also?

They're quite disciplined. I think they'll continue to be. I mean these are sensitive topics for us to talk about in these forums. But like we really, really, really appreciate the support that TSMC is giving us as a partner and the way that they're working with us to bring more capacity online to support not just the Helios and 450 growth, but the significant server TAM expansion that we're seeing and planning for, not just for this year, but for all of next year and conversations going into 2028 on wafer capacity to support demand.

So we really appreciate the work that they're doing on our behalf. And I think they're being, as they've always been, disciplined in the way that they bring capacity online and do their diligence 2 or 3 levels deep in the customer base to understand where the organic demand is coming from to support the capacity that they're bringing on.

Makes sense. And then the final one, Jean, is on just memory cost inflation, right? I guess when you wake up and see DRAM -- I'm sure DRAM price is not what you see when you wake up, but when you see that the cost inflation every day, is that a good thing because it gives you more pricing power? Or is that a bad thing because now you have to absorb that cost and hunt for more supply? What are kind of the pros and cons of that memory cost inflation for you?

It's really a great question. First, for us, right, we really want to make sure if there's a longer-term agreement with our suppliers, we can secure the capacity. That is the number one thing. When you have a large business at our scale, you have to plan ahead.

And secondly, we think more strategically. If there's memory cost increase, we do need to figure out a way to share with our customers to absorb that increase together. I think the most important thing for us is we actually don't want to use the shortage of control of memory to just increase the price. We want to make sure customers help us to absorb the cost increase, but we are more strategic with the long-term relationship.

I think from an industry perspective, the memory cost increase at this kind of level we have never seen, right? The price is really high. I think it does increase the CapEx spending for end customers where they spend the CapEx. I think in the longer term, just like all the industry, you always have a new capacity add up. You also have a new innovation on how you utilize memory more efficiently. It will figure out itself in the longer term. That's what we believe.

Got it. But do you have long-term agreements in place for the majority of your memory needs? Or do you think that this is a cyclical high, right, and maybe there will be kind of more normalization of that input cost?

Yes. From a planning perspective, because our manufacturing cycle is so long, if we plan for the MI450 ramp this year, we had to start to talk to our suppliers last year or year before. Similar situation is right now, not only with a very tight supply chain environment, not only we're planning 2027, we are planning 2028. So for us to continue to make sure we can support our ambitious plan to grow the company, we have to plan ahead.

Vivek, in the server market and in the PC market, the majority of -- the vast majority of the DRAM is actually acquired by our OEM and ODM partners and the hyperscalers themselves, right? That DRAM doesn't run through our P&L. And we work really, really hard with all of the customer base to try to make sure that we're matching like the memory supply that they're able to get and the CPU supply -- or the GPU supply that we're able to provide together to where there's not mismatching of supply in the industry or hoarding of anything. And so we've been working really hard on that.

But there's certainly increased input costs to end-devices in certain parts of the consumer and gaming market where there have been some anticipated market impacts. And I think we've given you guys our current view of that in the guidance that we've talked about. But we're working really hard on behalf of the whole customer base to try to make sure that the memory pricing and the matching of components is relatively seamless in a time like this.

Got you. And just final question before we close. When you look across your customer base, Jean, how far is the visibility like that they are willing to provide to you now? And when you get that, what are you doing differently in your business operationally to kind of deliver to those?

Yes. I think because the supply chain environment is really tight, customers really plan ahead. So we have a very good visibility into 2027 and beyond. I think our customers and ourselves, especially when you think about the data center investment, very large CapEx planning, very long planning cycle, they actually plan way ahead. 2027, we feel really good about both the visibility of the demand side and the visibility of the supply side. And of course, 2028 and beyond, that everybody is working on.

Okay. On that positive note, thank you so much, Jean.

Thank you.

Thanks, Vivek.

Appreciate your time.

Greetings, and welcome to the AMD First Quarter 2026 Conference Call. [Operator Instructions] And please note that this conference is being recorded. I will now turn the conference over to Matt Ramsay, Vice President of Financial Strategy and IR. Thank you, Matt. You may begin.

Thank you, and welcome to AMD's First Quarter 2026 Financial Results Conference Call. By now, you should have had the opportunity to review a copy of our earnings press release and the accompanying slides. If you have not had a chance to review these materials, they can be found on the Investor Relations page of amd.com.

We will refer primarily to non-GAAP financial measures during today's call. The full non-GAAP to GAAP reconciliations are available in today's press release and slides posted on our website.

Participants on today's conference call are Dr. Lisa Su, our Chair and CEO; and Jean Hu, Executive Vice President, CFO and Treasurer. This is a live call and will be replayed via webcast on our website.

Before we begin the call, I would like to note that Jean Hu will present at the Bank of America Global TMT Conference on Tuesday, June 2 in San Francisco.

Today's discussion contains forward-looking statements based on current beliefs, assumptions and expectations, speak only as of today and as such, involve risks and uncertainties that could cause actual results to differ materially from our current expectations. Please refer to our cautionary statement in our press release for more information on factors that could cause actual results to differ materially.

With that, I will hand the call over to Lisa.

Thank you, Matt, and good afternoon to all those listening in today. We delivered an outstanding start to the year driven by accelerating demand for AI infrastructure across our portfolio. Growth was broad-based with every segment increasing year-over-year, led by 57% data center revenue growth. First quarter revenue increased 38% year-over-year to $10.3 billion, earnings grew more than 40%, and free cash flow more than tripled to a record $2.6 billion, driven by significantly higher sales of EPYC CPUs, Instinct GPUs and Ryzen processors. These results mark a clear inflection in our growth trajectory and a structural shift in our business. Data center is now the primary driver of our revenue and earnings growth. And as AI adoption scales, demand is increasing not only for accelerators, but also for the high-performance CPUs that power and orchestrate those workloads.

Turning to our segments. Data center revenue increased 57% year-over-year to a record $5.8 billion, led by strong demand for our EPYC CPUs and Instinct GPUs. In server, we delivered our fourth consecutive quarter of record server CPU revenue. Revenue increased more than 50% year-over-year with sales to both cloud and enterprise customers each growing more than 50%. Share gains accelerated year-over-year, reflecting the ramp of fifth-gen EPYC Turin CPUs and continued strength of fourth gen EPYC processors across a wide range of workloads. In cloud, AI was the primary driver of growth in the quarter as every major cloud provider expanded their EPYC footprint to support a broad range of AI workloads from general purpose compute and data processing to head nodes for accelerators and emerging agentic applications. EPYC-powered cloud instances increased nearly 50% year-over-year to more than 1,600 with instances optimized for virtually every enterprise workload and expanded availability across the largest global cloud providers. In enterprise, demand accelerated with record revenue and record sell-through in the quarter. We expanded our customer base with new wins across financial services, health care, industrial and digital infrastructure companies while also building momentum with mid-market and SMB customers. We are well positioned to continue gaining share as more enterprises standardize on EPYC across on-prem and hybrid environments based on our leadership performance and TCO.

Looking ahead, our sixth-gen EPYC Venice processor built on our Zen6 architecture and 2-nanometer process technology is designed to extend our leadership across cloud, enterprise and AI workloads. The Venice family spans a broad set of CPUs optimized for throughput, performance per watt and performance per dollar, including Verano, our first EPYC CPU purpose built for AI infrastructure. Across the portfolio, Venice widens our competitive advantage, delivering substantially higher performance per socket and per watt versus competitive x86 offerings and more than 2x throughput per socket versus leading ARM-based AI solutions. Customer demand is very strong with more customers validating and ramping platforms at this stage than with any prior EPYC generation, and we remain on track to launch Venice later this year.

Looking more broadly, we are seeing a meaningful acceleration in customer demand driven by the rapid scaling of AI workloads across both cloud and enterprise. Inferencing and agentic AI are increasing the need for server CPU compute as these workloads require additional CPU processing for orchestration, data movement and parallel execution in addition to serving as the head notes for GPUs and accelerators. As a result, we are seeing both stronger near-term demand and deeper engagement with customers on long-term capacity planning.

At our Financial Analyst Day in November, we outlined the server CPU market growing at approximately 18% annually over the next 3 to 5 years. Based on the demand signals we are seeing today and the structural increase in CPU compute requirements driven by agentic AI, we now expect the server CPU TAM to grow at greater than 35% annually, reaching over $120 billion by 2030. In response to this demand, we are working closely with our supply chain partners to meaningfully increase our wafer and back-end capacities to support this growth. As a result, we now expect server CPU revenue to grow by more than 70% year-over-year in the second quarter, with robust growth continuing through the second half of 2026 and into 2027 as we ramp our next-generation EPYC processors.

Now turning to our data center AI business. Revenue grew by a significant double-digit percentage year-over-year as adoption of Instinct accelerates across cloud, enterprise, sovereign and supercomputing customers. We're seeing strong momentum as customers move from pilots to large-scale production deployments, particularly in inference where our leadership memory capacity and bandwidth are key advantages. This momentum is driving deeper long-term customer engagements, including large-scale multi-generation deployments.

A key example is our expanded strategic partnership with Meta to deploy up to 6 gigawatts of AMD Instinct GPUs spanning several product generations. Our agreement includes a custom GPU accelerator based on our MI450 architecture, co-designed to support Meta's next-generation AI workloads. Shipments are on track to begin in the second half of the year, leveraging our Helios rack scale architecture, which integrates Instinct GPUs with EPYC Venice CPUs to deliver fully optimized high-performance AI infrastructure.

Together with our previously announced OpenAI partnership, these engagements position AMD as a core partner to the world's largest AI infrastructure builders with deep co-engineering relationships and multiyear visibility into large-scale deployments. More broadly, Instinct adoption continues to expand across AI native and enterprise customers for both training and inference workloads. Existing partners are expanding Instinct across a broader set of workloads, while a growing number of new partners are deploying production AI workloads on Instinct, highlighting the maturity of our hardware and software stack.

On the software front, we continue to make strong progress with ROCm, improving performance, scalability and enabling customers to reach production faster. In our latest MLPerf results, MI355X delivered strong competitive performance across the full suite with leadership results in multiple categories. We also expanded day 0 support for the leading open models, including the latest Google Gemma 4 family, Qwen, Kimi and others, enabling customers to deploy new models quickly with optimized performance.

To build on this momentum, we have significantly accelerated our ROCm development cadence through increased software investments and agent-based coding workflows, enabling faster performance improvements and more rapid deployment of new capabilities. Looking ahead, customer pull for Helios is very strong, driven by our leadership performance, memory bandwidth and scale out capacity. Helios development is progressing well with strong execution across silicon software and systems as we advance through key milestones. We have begun sampling MI450 series GPUs to lead customers and remain on track to ramp Helios production shipments in the second half of the year. As we approach production, demand for MI450 series GPUs continues to strengthen, with lead customer forecasts now exceeding our initial plans and a growing number of new customers engaging on large-scale deployments, including additional multi-gigawatt opportunities. With this expanded visibility, we have strong and increasing confidence in our ability to deliver tens of billions of dollars in annual data center AI revenue in 2027 and to exceed our long-term growth target of greater than 80% in the coming years.

I look forward to sharing more on our next-generation Instinct GPUs, EPYC processors, Helios rack scale platform and our growing customer engagements at our Advancing AI event in July.

Turning to client and gaming. Segment revenue increased 23% year-over-year to $3.6 billion. In client, revenue grew 26% year-over-year to $2.9 billion, led by strong sales of our latest Ryzen processors and continued share gains across consumer and commercial markets. In desktop, we strengthened our Ryzen lineup, including our latest X3D processors that deliver leadership performance across gaming, content creation and professional workloads. We also introduced the Ryzen AI 400 series and Ryzen AI Pro 400 series desktop CPUs -- our AITC offerings across both consumer and commercial systems. In mobile, we delivered strong growth driven by a richer product mix as Ryzen 400 mobile PC shipments ramped and commercial adoption increased. Commercial was a key highlight in the quarter with sell-through of Ryzen Pro PCs increasing more than 50% year-over-year as Dell, HP and Lenovo broadened their AMD offerings. We also closed new enterprise wins across large technology, financial services, health care and aerospace customers. Looking ahead, we expect demand for our Ryzen CPUs to remain solid in the second quarter. However, we are planning for second half PC shipments to be lower due to higher memory and component costs. Against this backdrop, we still expect our client revenue to grow year-over-year and outperform the market, driven by the strength of our Ryzen portfolio and expanding commercial adoption.

In gaming, revenue increased 11% year-over-year to $720 million. Semi-custom revenue declined year-over-year as expected at this stage of the console cycle, while engagements with customers on next-generation platforms remain strong. In graphics, revenue increased year-over-year led by demand for our latest generation Radeon 9000 series GPUs. We also strengthened our Radeon portfolio with updates to our FSR software that improved performance and digital quality across a broad set of gaming workloads. Similar to the PC market, we believe that second half demand in gaming will be impacted by higher memory and component costs, and we are planning the business accordingly.

Turning to our Embedded segment. Revenue increased 6% year-over-year to $873 million, driven by strength in test, measurement and emulation, aerospace and defense and communications as well as increased adoption of our embedded x86 products. Design win momentum grew by a double-digit percentage year-over-year with billions of dollars in new wins across markets, reflecting the continued expansion of our embedded business from a primarily FPGA-focused portfolio to a broader set of adaptive embedded X86 and semi-custom solutions significantly expanding our TAM. Our semi-custom engagements also expanded in the quarter as data center, communications and other embedded customers leverage our broad IP portfolio and high-performance expertise to build differentiated solutions.

In summary, our first quarter results mark a clear step-up in our growth trajectory with accelerating momentum across the business. Our client business continues to outperform the market, driven by rise in adoption and share gains, while an embedded design win momentum and demand are strengthening across our expanded adaptive and x86 portfolio. At the same time, our data center business is inflecting with strong demand for both EPYC and Instinct products significant growth.

While we are still in the early stages of the AI infrastructure cycle, the pace and scale of deployments we are seeing today reinforce both the magnitude and durability of the opportunity ahead. As inferencing and agenetic AI deployment scale, they are fundamentally increasing compute requirements, driving both larger scale accelerator deployments and significantly more CPU compute. AMD is uniquely positioned to lead in this next phase of AI with leadership products across high-performance service CPUs and AI accelerators and the ability to optimize them together as fully integrated rack scale solution. We have a world-class supply chain and are making significant investments to expand capacity and execute at scale. With the momentum we are seeing across the business and the expanding market opportunity, we see a clear path to exceed our long-term financial targets, including delivering more than $20 in EPS over the strategic time frame.

Now I will turn the call over to Jean to provide additional color on our first quarter results. Jean?

Thank you, Lisa, and good afternoon, everyone. I'll start with a review of our first quarter financial results and then provide our current outlook for the second quarter of fiscal 2026. We are pleased with our outstanding first quarter results delivering accelerated revenue growth and earnings expansion driven by strong execution and operating leverage. First quarter revenue was $10.3 billion, exceeding the high end of our guidance, growing 38% year-over-year, driven by strong growth in the data center and client and gaming segments and the return to growth in the embedded segment. Revenue was flat sequentially with continued growth in the data center segment, offset by seasonality in the client and the gaming segment and the embedded segment. Gross margin was 55%, up 170 basis points versus a year ago, driven by a favorable product mix, including a higher data center revenue contribution. Operating expenses were $3.1 billion, an increase of 42% year-over-year as we continue to invest in R&D to support our AI road map and the long-term growth opportunities and go-to-market activities.

As the business scales, operating income grew faster than top line revenue. Operating income was $2.5 billion, representing a 25% operating margin. Taxes, interest and other result in a net expense of approximately $275 million. For the quarter, diluted earnings per share was $1.37, up 43% year-over-year, underscoring the significant operating leverage in our model as we scale.

Now turning to our reportable segment starting with the data center segment. Revenue was a record $5.8 billion, up 57% year-over-year and 7% sequentially, driven by strong demand for EPYC processors and the continued ramp of Instinct GPUs. Data center segment operating income was $1.6 billion or 28% of revenue compared to $932 million or 25% a year ago. Client and gaming segment revenue was $3.6 billion, up 23% year-over-year. On a sequential basis, revenue was down 9%, consistent with seasonality. The client business revenue was $2.9 billion, up 26% year-over-year, driven by strong demand for our latest -- processors, favorable product mix and continued share gains across consumer and commercial markets. Sequentially, client revenue was down 7% due to seasonality. The gaming business revenue was $720 million, up 11% year-over-year, primarily driven by higher demand for Radeon GPUs, partially offset by lower semi customer revenue. Sequentially, gaming revenue was down 15%, consistent with our expectations.

In addition, as Lisa mentioned earlier, we expect second half demand in gaming to be impacted by higher memory and component costs. We now expect second half gaming revenue to decline more than 20% compared to the first half.

Client and gaming segment operating income was $575 million or 16% of revenue compared to $496 million or 17% a year ago. Embedded segment revenue was $873 million, up 6% year-over-year as demand strengthened across several end markets. Sequentially, embedded revenue was seasonally down 8%. Embedded segment operating income was $338 million or 39% of revenue compared to $328 million or 40% a year ago.

Turning to the balance sheet and the cash flow. During the quarter, we generated $3 billion in cash from continuing operations and a record $2.6 billion in free cash flow or 25% of revenue, demonstrating the cash-generating power of our business model. Inventory was roughly flat at $8 billion. At the end of the quarter, cash, cash equivalents and short-term investment was $12.3 billion.

In the quarter, we repurchased 1.1 million shares and returned $221 million to shareholders. We ended the quarter with $9.2 billion authorization remaining under our share repurchase program.

Now turning to our second quarter 2026 outlook. We expect revenue to be approximately $11.2 billion, plus or minus $300 million. At the midpoint of our guidance, revenue is expected to be up 46% year-over-year driven by a very strong growth in our data center segment, growth in our client and the gaming segment and a double-digit growth in our embedded segment. Sequentially, we expect revenue to be up approximately 9% driven by double-digit growth in both our data center and embedded segments and modest growth in our client and gaming segment.

In addition, we expect second quarter non-GAAP gross margin to be approximately 56%, non-GAAP operating expenses to be approximately $3.3 billion, non-GAAP other income and expense to be a gain of approximately $60 million. Non-GAAP effective tax rate to be 13%, and the diluted share count is expected to be approximately 1.66 billion shares.

In closing, the first quarter of 2026 was an outstanding quarter for AMD, reflecting strong momentum across the business with accelerated revenue and earnings expansion. We are very well positioned to build on the momentum as we scale our data center business, expand margins, drive continued earnings growth and the long-term shareholder value creation.

With that, I'll turn it back to Matt for the Q&A session.

Thank you, Jean. Operator, we're ready to start the Q&A session now. [Operator Instructions] But please go ahead and poll for questions. Thank you.

Thank you, Matt. We will now be conducting a question-and-answer session. [Operator Instructions] And the first question comes from the line of Joshua Buchalter with TD Cowen.

Congrats on the results. Actually, I'm going to start with CPUs, which hasn't happened in a bit. It hasn't been that long since you announced the $60 billion server CPU TAM for 2030 at the Analyst Day, and it's very quickly doubled. Agentic AI has obviously gotten a lot of attention in recent months, but it would be helpful to hear your thoughts on how this TAM is inflecting and changing so meaningfully in such a short amount of time. And maybe you could also speak to your confidence in hitting that greater than 50% share target from the Analyst Day as your x86 competitor seems to be improving its supply and also there seems to be more momentum on the merchant and custom arm CPU side.

Yes. Sure, Josh. Thanks for the question. So first of all, back to the -- when we think about CPU TAM, I mean we've always said that CPUs are very critical part of data center infrastructure, and that's been where we've invested. And we saw the first signs of, let's call it, AI demand really pulling CPU demand last year, and that was the reason we updated the TAM to, let's call it, the 18% CAGR or approximately $60 billion. And what we've seen is all of the things that we believed in terms of agentic AI and inferencing and all the CPU compute that is required, is just happening, and it's happening at a much faster pace. So over the last, let's call it, the last few months, as we've talked to our customers and we've seen how AI adoption is really unfolding, we're seeing significant more CPU demand from really every major cloud provider as well as enterprise customers. And the way that comes across is as AI adoption scales, you need more inferencing. As inferencing scales and you do more -- you have more agents and agentic AI, they all require CPUs for all of the orchestration and the data processing and these other tasks. So with that, we've looked at it both bottoms up in terms of talking to customers and having them give us longer-term forecasts as well as just doing some clear workload analysis. And yes, I mean, it's a very exciting TAM. I think it's exciting to see CPUs growing greater than 35% to over $120 billion.

And then when you think about AMD in the context of that, I mean, CPUs are critical for so many tasks that you are seeing a lot more discussion about CPUs in the market. But we actually view it in 3 categories, right? There's general purpose compute. There's the head nodes that really support the AI accelerators. And then there are CPUs just for all of the agentic AI work. And to do all of this, our belief is you need a broad portfolio of CPUs, and that's really what we have been focused on is building not just one type, but really broader in terms of throughput optimized, power optimized, cost optimized, AI infrastructure optimized as we've done in the Venice family. So when you put all that together, we're very excited about the larger TAM, and we're also very happy with the traction that we're getting. We're clearly feeling like we're seeing significant share gain as we're going into our Turin portfolio that has ramped very nicely. Venice is extremely well positioned, and we're working with customers right now on -- beyond Venice and what we're doing in those architectures. So we feel really good about the market as well as our opportunity to grow to greater than 50% share of that market.

I wanted to ask about the Instinct side. So in the press release, you mentioned that MI450 and Helios engagements are strengthening with customer forecast exceeding the expectations and the pipeline growing. You certainly have the big public OpenAI and Meta deals. Was this comment referring to those engagements upsizing versus the announced initial deployments? Or was it other customers and maybe the increase on the MI450 time line? Or is it MI500 and beyond?

Sure, Josh. So we are very excited about MI450 on Helios. We're seeing significant customer interest in those products as well. We have certainly talked about our large partnerships with OpenAI and Meta, and those are going really well. We appreciate the deep co-engineering that is not on there.

When we look at the totality of, let's call it, based on our current visibility, how those forecasts are coming in with all of our customers, we're actually seeing it above our initial plans that we had planned for 2027. And I think the encouraging thing is we're seeing a breadth of customers who are now very interested in deploying at significant scale MI450 series. And those are for both training and inference workloads, although the largest deployments are for inference. And based on all of that and the scale of new customer interest, we see a path to really get to exceed our original targets of greater than 80% CAGR.

And these are really 2027 time frame. Obviously, when we talk to customers, we're talking to them about MI355. There's a lot of good traction we're seeing there. MI450 and Helios, I think for significant large-scale deployments; and then many customers are also very engaged with us on the MI500 series and all of the opportunities there. So we feel like very, very good progress. And the key is that we're continuing to broaden and widen the scope of both customers as well as workloads.

And the next question comes from the line of Thomas O'Malley with Barclays.

Lisa, if I get your numbers correct here in the March quarter, it sounds like the server processor side of the CPU side grew over 50%. If you take it just at the word, it looks like maybe the data center GPU side actually grew in Q1. So I was curious around the cadence of this year kind of previously, you had talked about really a back half weighted and then kind of more so Q4 weighted here. Could you talk about if that's changed at all?

And then the second part of the question is, as you go into 2027, clearly, you're pointing out a lot of upside from the larger customers and then kind of the ecosystem around them with new customers as well. But when you look at supply, that's a major issue in the ecosystem today, could you talk about where you're concerned on supply, if you are? And then any gating factors as you look into next year, whether that be power, data center build-outs, et cetera? Or do you feel really good about the ability to grow?

Yes. Okay. A lot of pieces of that question, Tom. So let me try to get through it. So first of all, on the data center segment in Q1, the server business was greater than 50% year-over-year as we said in the prepared remarks. The data center AI was actually down modestly because of the China transition. We had more China revenue -- I'm sorry, sequentially more China revenue in Q4, and it was less in Q1. But as we go forward, I think we see strong growth in both segments. So we guided data center Q2 up sequentially double digits, and that's double digits in both server as well as data center AI.

And the progression as we go forward. So first, on the server CPU side, we talked about growing to over 70% year-over-year in Q2, and that continuing into the second half of the year. And on the data center AI side, we will be ramping Helios in the second half of the year, so let's call it, starting with initial volume in Q3 with a significant ramp in Q4 and then continuing to ramp in Q1. So that's kind of a little bit of progression.

And then to your questions about customers and supply, I think I answered, Josh, the customer question. I think we have very good visibility now into the deployments that are on track for 2027. And when I say good visibility, it's visibility down to which data centers are the GPU is going to be installed in. And so that's necessary just given all of the constraints out there. We feel that there is tightness in the supply chain, there's certainly tightness in sort of data center build-outs, but we are confident in our ability to supply to the levels of growth that we're talking about and to exceed the levels of growth that we're talking about. And we're also working very closely with our customers and our partners to ensure that we have good visibility to data center power. And there is much more power that's coming online in 2027. And so with all those things in mind, I think, again, lots of things to manage. It's a complex ramp, but we're very pleased with the progress on the ramp.

All right, Tom, I think you shotgun approached the multiple questions there. So operator, maybe we can go on to the next call, please. Thank you.

The next question comes from the line of Ross Seymore with Deutsche Bank.

The first one is just on the EPYC competition. Lisa, you went through some of the statistics of you versus x86 and you versus ARM, but I wanted to dive a little bit deeper into that. How do you see AMD truly differentiating, especially when you're signing -- well, you see some of your competition signing up the same customers from the ARM side and the x86 competition having more supply. So I just I wanted to see if you could dig a little bit deeper into how you think the market share is going to trend over time?

Ross, look, we're very engaged with every major hyperscaler and in terms of understanding their needs on the CPU side. I think we have very much wanted to, let's call it, optimize our CPU road map for the various workloads. I think we were early to call this AI component of CPUs. And so we've been actually optimizing very closely with those customers.

The way to think about this, Ross, is that you're going to need a broad portfolio of CPUs, like not all CPUs are the same. Frankly, you're going to need different CPUs for whether you're talking about general purpose operations or you're talking about head nodes or you're talking about agentic AI tasks, they're going to be optimized differently. And we thought through that, and we are absolutely optimizing across the various workloads. So from a competitive standpoint, we feel very good about where things are. And from a deep relationship with the customer set, I think we feel very good about that. So from our current standpoint, I think the depth of our road map just expands as we go forward. And you shouldn't think about it as people are going to do one or the other. I think you're going to see people actually use x86 and ARM for many of the large hyperscalers. And even for those who are developing their own, they're still buying lots of CPUs in the merchant market for the reason that I just stated, which is unique different CPUs for the different types of workloads, and there's very high demand at the moment.

I guess for my follow-up, maybe more for Jean on the gross margin side of things. It's nice to see the gross margin popping up in the second quarter again. But I just wanted to get some trends longer term, maybe not specific numbers, but how should we think about when Helios and the Instinct side really ramps in the fourth quarter and more so next year. I could see some offsets with that carrying a below corporate average gross margin, but then everything that Lisa talked about with the EPYC side of things being significantly stronger might be more of an offset than it was in the past. So just walk us through the puts and takes of that and maybe directionally where you think gross margin goes over the next year or 2?

Yes, Ross, thanks for the question. We are very pleased with how our gross margin is trending. It came in really strong in Q1. And also, as you mentioned, we guided Q2 higher at 56%. I think as we think about the second half quarter-over-quarter, as you know, there are some puts and takes, right? I would just say, from a tailwind perspective, we actually have multiple tailwinds really are going to help our gross margin. First is the server CPU. Lisa talked about the service CPU expected to grow more than 70% in Q2 and continue to be really strong in second half. That really helps our gross margin.

Secondly, in the second half for gaming actually is going to come down, and our client business actually continued to go up the stack. So from a client and gaming segment, the gross margin actually is going to be also very helpful. Embedded actually is very accretive to our gross margin. Its momentum actually is continuing in the second half. So we are really pleased with all the tailwinds we have. On the other side, MI450 will start to ramp in Q3 and then ramp significantly in Q4. That is below corporate average. So that will have different puts and takes in Q4 in the gross margin side.

But when we sit here, when we look at all the positive trends we have to really offset some of the gross margin dilution from MI450 side, we actually feel really good about the setup of the gross margin for 2026. And into next year, I think some of the tailwinds I talked about that will actually continue. That's why we feel confident about continue to drive the gross margin. We actually, during our financial Analyst Day, we outlined the long-term gross margin in the range of 55% to 58%. We think for the first year, we are making good progress there.

And the next question comes from the line of Timothy Arcuri with UBS.

I wanted to ask about units versus ASP for server CPU. If I look at the June guidance, it sort of implies up 25% to 30% for server CPU. And Lisa, you had mentioned second half of the year. It sort of implies that server CPU could grow like 70%, maybe a little more this year. And so I guess my question is, how much of that growth either in June or for the year, like units versus pricing? Is the -- are these price increases sort of mostly captured in June? Or is that also helping in the back half of the year?

Yes. Tim, the way I would say it is, maybe let me bring you back to Q1 for a moment. So if you look at our significant growth in the server business, it was actually -- although we were up on a year-over-year basis for both ASPs and units, it was actually much more unit driven. So we are shipping more CPUs across not just the high-end Turin family, but we're actually shipping a lot of generalist sort of the Zen 4 family as well.

As we go forward for Q2 and into the second half, we are guiding for a significant amount of growth. I think there's a little bit of ASP in there, but the way we're thinking about pricing to be fair, is we are in a range where the supply chain is tight. And so there are some inflationary pressures. Costs have gone up a bit, and we are sharing some of that with our customers. But we are also being very thoughtful in -- look, this is -- we're playing out for the long term, and that means that we are -- our goal is to ship more units and a lot more units. And so from that standpoint, you should imagine that the majority of the growth is unit driven, and the ASPs are just really to help cover some of the inflationary pressures.

And just to add to what Lisa said, our ASP is increasing because of the mix where actually each new generation, the call counts, those are increasing, that actually drives the ASP up.

And then I guess, Lisa, also -- so there's a lot of new architectures that are being used from multi-tenancy all the way to low latency. And your competitor has talked about the low latency part of the market being 20% plus and they, of course, added to their portfolio there. Can you talk about how you see that part of the market? I mean, obviously, you have enough business now you don't need to worry about that probably for now. But can you talk about that?

Yes, sure. So look, I think what we're seeing is what we expected in the sense that as you go -- as the AI adoption continues and the volumes continue to go up and the overall market goes up, you are going to see, let's call it, different compute architecture is being used because you want to get more cost optimization from that. So we expect that even in that situation, obviously, the vast majority of the TAM is still going to be, let's call it, data center GPUs as the primary accelerator. But you may choose to do optimization around inference, around lowly, around certain parts of the stack, whether it's decode versus prefill, I think that's very natural.

The way we look at it is we're developing a full compute portfolio. So that CPUs, that GPUs, that's the ability to connect to all accelerators as well as the ability to customization for certain customers, and we've also talked about our semi-custom capabilities. And with all of those sort of compute capabilities in our tool chest, I think we will be able to address, very effectively, a large portion of this market, including the low latency portion of the market. So from our standpoint, this is kind of a natural evolution.

Now how fast it goes depends a bit on the technology in terms of what share of the TAM these things become, but we should expect that there will be different variants, and we're well prepared to address those different variants.

And the next question comes from the line of Vivek Arya with Bank of America.

Lisa, do you think agentic CPU growth is incremental? Or is it coming at the expense of GPUs conceptually? So if you're raising server CPU TAM, are you also implicitly kind of raising AI TAM? So just I'm interested in your perspective on what did you think server CPU was as a percentage of AI TAM before? And what is it now with this $120 billion number?

Sure, Vivek. So the way we're thinking about is it's largely additive to the TAM. So you should think about we need all of the accelerators to run these foundational models, and then as these agents do work, they spawn more CPU tasks. So I would say largely incremental. The key is to make sure what we're seeing is in these deployments, the key is to make sure the ratio of CPUs to GPUs are the right ratio. So if you're installing a gigawatt of compute, the ratio -- there's a percentage of CPU as part of that gigawatt will increase. Some of the conversation in the industry has been about CPU to GPU ratios. And it's very hard to call exactly, but we certainly see the movement towards where in the past, the CPU to GPU ratio was primarily just as a host node in like a 1:4 or 1:8 configuration node, now changing and getting closer to a 1:1 configuration or even -- you can even imagine if you get lots and lots of agents that you could have more CPUs and GPUs. So -- but all -- all in all, to answer your question, I think it's largely additive to the TAM. And the key is that everyone is now planning and thinking about CPUs at the same time that they're thinking about their accelerator deployments, which is a good thing.

All right. And from my follow-up, Lisa, we continue to see memory prices go up. I imagine that is both kind of a cost inflation for you but perhaps an opportunity to take price as well. I'm curious, how is that dynamic playing out for AMD? And especially for your customers because a greater part of their CapEx increase is really kind of this memory inflation tax, right, that they have to pay. So how is this dynamic playing out for you and for your customers? And the part that I'm very interested in is that have you secured enough supply versus your other larger competitor who has disclosed a lot of prepayments and other things? So just how is this memory inflation dynamic playing out? And are you kind of adequately supplied from that perspective?

Sure. So Vivek, let me answer the second one first. I think from a supply standpoint, we are very happy with our partnerships with the memory vendors, and we have secured enough supply to certainly meet and exceed our targets. So it is a tight memory environment. Let me be clear. But I think we are very deep partnerships with the memory providers.

And then back to your comments on the inflationary pressures. I mean, look, this is something that everyone in the industry is working with in the time of tight supply, we are seeing some cost increases on the memory side. I think we are all working through that. The way we're seeing it unfold in the market is actually on the data center side, because of the, let's call it, the demand for AI compute, I mean people are largely focused on supply and ensuring that the supply assurance is there. The corollary of that, the larger impact that we're watching is the impact on the consumer markets. And as we said in the prepared remarks, we are expecting that there could be some demand -- sort of the demand impact as a result of the memory price increases on things like the PC business in the second half of the year as well as the gaming business. So we're taking that into account in our overall model. And we continue to work closely with the memory providers as well as our customers to ensure that every time we ship a CPU or GPU, then it's paired with the memory on the other side so that we don't have compute that is not being deployed.

And the next question comes from the line of Aaron Rakers with Wells Fargo.

Congrats on the results. I want to stick on the topic of CPU to GPU. And as we think about the chart that you had outlined at the Analyst Day, there was obviously broken out between traditional CPUs and then the AI bucket on top of that. Obviously, I think the new forecast has a lot to do with the AI CPU expansion. I'm just curious, when you're doing a CPU in an AI workload, is there structurally a different level of ASP tied to that kind of CPU optimized for AI relative to a general purpose server CPU? Any kind of color or help on that would be useful.

Sure, Aaron. So let me start with the broader question. The broader question regarding -- the way we think about the CPU TAM is, again, think about it as 3 categories. So there is a traditional CPUs, let's call it, general purpose CPU TAM that is increasing, but let's call it, increasing at low rate, maybe, let's call it, low double digits, then you have your AI head node, which is connecting to accelerators, which is also growing, but it's smaller. And then the largest piece of the growth is this agentic AI piece, which we think is really stemming from all of the agentic processes. I don't have a number that I can tell you in terms of relative ASPs because it really depends on the workload that is being run. And what we see going forward is as core counts increase, obviously, we will see ASP increase. And that's the direction that we're going in as we go forward. But the main point is the largest portion of this is the agentic AI, the CPUs that are serving these agent AI workloads in terms of the TAM increase.

And as a quick follow up, I'm curious, how do you characterize the competitive landscape as we see some of the ARM introductions in the market. Just curious of your views on the competitive landscape and server CPU.

Yes. Aaron, the best way to think about the server CPU landscape is, again, number one, everyone is talking about CPUs. So that tells you how critical they are for the AI infrastructure. And I think that's a good thing. We feel like we're very well positioned. No question, ARM is good architecture. It has a place in the data center market. We view it as more point products relative to a portfolio, where, from an AMD standpoint, we've built this broad portfolio of CPUs, going forward, what you're going to need for all of these different workloads. And we have, in the Venice time frame, added an AI-optimized CPU with the Verano in addition to our throughput optimized and sort of cost optimized point. So from that standpoint, I think we're very competitive. We're continuing to innovate on architecture. We're continuing to innovate on both advanced packaging as well as all of the architectural pieces. So we feel very well positioned going forward. And the key is the TAM is much, much larger than anybody thought. And so there's a lot of opportunity for different products to be successful in this area.

And the next question comes from the line of CJ Muse with Cantor Fitzgerald.

I guess first question, I was hoping to speak a bit more about client for all of calendar '26. You talked about growth -- expected growth, but would love to hear your thoughts around seasonality in the second half. And I'm assuming that you are repurposing certain logic tiles from clients over to the data center and would love to kind of better understand what the implications are for ASPs on the client side looking into the second half.

Sure. So CJ, I think the client business has performed really well for us. I think if we look at Q1, it actually was a little bit stronger than what we expected. We are seeing some mix shift in the client business. The mix that we're seeing is the M&C or the notebook business is actually growing, especially the premium portion. We're making very good progress in the commercial PC arena with our AI PCs. We did see desktops a little bit softer just given desktop is a more consumer-focused market. And so in that market, it's more impacted by some of the memory pricing and the component price increases.

When we look at the full year, our commentary is we are planning for some demand impact in the second half due to the memory pricing. But even in that environment, what we're focused on is ensuring that we continue to make good progress on the commercial business and continuing to focus on the premium segments of the market. So we believe that we will continue to grow on a year-over-year basis for the client business compared to last year. And as it relates to ASPs, again, it's a little bit of puts and takes between notebook and desktop. But overall, I think we're feeling good about our opportunity to outperform the market and clients going forward. Does that answer it?

That was perfect. And then I guess a question on Instinct gross margins. With compute essentially sold out and obviously, you're building a business, so one has to be, I guess, conservative on that front. But I would think outside of kind of passing through HBM that given the very tight wafer environment that this would be a place where you could look to drive your Instinct margins closer to your corporate average? How are you thinking about that either today or in the coming 1, 2, 3 years?

CJ, at this stage, we really focus on driving the top line revenue growth on our Instinct family for product. I think on the gross margin side, you're absolutely right, it's really -- the demand for compute is tremendous. We actually are very strategic in how we think about the -- how we work with the customers. And of course, the different customers also have a different gross margin. I think, over time, once we start to ramp our revenue, we'll have a lot of opportunities to improve gross margin, both on the ASP side, but also, more importantly, on the cost side when we scale our business.

And the next question comes from the line of Stacy Rasgon with Bernstein Research.

For the first one, I just wanted to make sure I have the near-term AI GPU trajectory correct. So I know you said it was down sequentially in Q1 because of China. You had like $390 million of China revenue in Q4. So the AI business in Q1 actually grow sequentially ex China because it doesn't feel like it given the server outlook? And then I look at what's maybe suggested for Q2, are you thinking GPUs and servers kind of grow similar rate sequentially because it would probably put GPUs in Q2 below the overall revenue in Q4, which seems low to me. I'm just trying to tie all that out. Could you help me with that, please?

Yes. So I think, Stacy, I appreciate the question. I think if you look at Q1, we did mention data center AI was down modest pace sequentially primarily due to lower China revenue in the quarter. I think on your second question regarding Q2, you're right, both data center AI and the server will grow double digit in Q2.

Yes. But you didn't answer my question. In Q1, did it grow sequentially ex the China step down, I guess, is what I'm asking.

The China, for our business, in Q1, it's not material. So I think I will repeat what I just said. Yes, the revenue -- the China revenue in Q1 is not material.

Okay. Okay. So you don't want to -- okay. Second question, OpEx [indiscernible] for spending -- but it sort of continues to blow past the targets. You kind of give an OpEx guide and then it blows through it and then you guide higher. So again, I'm not bothered by the [indiscernible]. I'm just wondering why is the OpEx been so hard to forecast? And how should we be thinking about that through the rest of the year in revenue growth?

Yes. Thanks, Stacy, for that question. I think the most important thing is given the tremendous market opportunities we have, we actually are investing aggressively. If you look at the past several quarters, we're really leaning in, in investing, but all the AI investments are driving the revenue momentum. So if you look at the Q1, revenue was 38% up; but then Q2, we guided 46% up. The investments are driving the revenue momentum. Some of the OpEx increase, of course, is tied to the revenue. When you look at our bid on the revenue side versus our guidance, we did bid on the revenue side, right? So that impacted a little bit. But also, at the same time, we have a lot of customer engagement with our data center AI business, we do continue to make sure we have the resource to support our different customers.

Thank you very much. Operator, I think we have time for one more caller on the call. Thank you.

Our final question comes from the line of Blayne Curtis with Jefferies.

Lisa, I just want to go back to the supply side. There was a lot of story about your competitor restarting 7-nanometer. I'm just kind of curious as you look at that landscape which is quite robust through the decade, do you think that the older products will stay around longer? And is there a way to think about the implications for gross margin in such a strong market. Is that actually a negative?

Actually, Blayne, I don't think we see the older products hanging around longer. In our case, I think it might be company-specific stuff. In our case, we actually see -- first of all, Turin is very strong. We actually crossed over 50% of our revenue being Turin this quarter. Generalist is very strong. We're still shipping some along, but I would say that's come down over time. So in general, people want to use the newer products because they're just more efficient in every aspect from performance, from cost structure, from a power standpoint. So that's what we're seeing.

By the way, I should also mention, in addition to what we're seeing in the cloud segment of server, we're seeing really nice strong pickup in enterprise. And there as well, we're seeing our newer products do very well. So from our standpoint, it is all about ensuring that we ship what the customer needs. And in this case, it typically is our newer products, and we expect that to continue.

As we transition into Venice later this year, we will expect Turin and general to continue shipping, but there's a lot of goodness in going to the new products.

And on the supply chain side, I know there's been a lot of discussion about how tight the supply chain is. The supply chain is tight. I would definitely say that. But I also think this is an area where we excel. We have very deep relationships across the supply chain on the wafer side, on the back end capacity side. And we are seeing meaningful improvements in that. And as our customers come to us with more demand, we are getting more supply. And the good thing about this is we're now talking about '27 CPU demand, we're talking about '28 CPU demand. And so that allows us to just plan much better as we go forward.

And then just a quick one for Jean. I'm just curious to follow up on Stacy's question on OpEx. I guess I was a little surprised that SG&A is kind of outpacing R&D. I was just kind of curious, is that start-up costs, because in a strong market, you wouldn't think you would have to discount or have a big sales effort. So I'm just kind of curious for the year, how you think about R&D growth versus SG&A?

I think for the year, you should expect us to grow R&D much faster than SG&A. I think in the past few quarters, we have been really building our go-to-market machine, and we have been investing more in sales and marketing side. But going forward, you should expect the year-over-year growth R&D will grow faster than SG&A growth.

Yes. And if I just add to that, Blayne, the places that we invest -- Jean is absolutely right. We're investing in R&D ahead of sales and marketing. But the places that we're investing in sales and marketing are paying off. So the investments are going into enterprise servers. They're going into commercial PCs. They're going into mid-market, small and medium business. These are places where AMD traditionally didn't invest, but now that we have a much broader portfolio, both on the server CPU and on the commercial PC side, it makes sense for us to invest because that's sort of the very best part of those markets.

All right. Thank you very much, everybody, for joining and your interest in AMD. John, you can go ahead and close the call now. Thanks.

Thank you. And ladies and gentlemen, that does conclude the question-and-answer session, and that also concludes today's teleconference. We thank you for your participation. Please disconnect your lines, and have a wonderful day.

All right. Good morning. Welcome back, everybody. I'm Joe Moore, Morgan Stanley Semiconductor Research. And one of the real highlights of this conference for me, CEO of AMD, Lisa Su, is here right on the back of signing a major deal with Meta. So very excited for this conversation. Lisa, thank you for coming.

Thank you, Joe.

Maybe before we go into details, let me just start off with the overview of what you're excited about for 2026, how you think this year is going to play out.

Great. Well, first of all, it's great to be here. Nice to see everyone bright and early at 7:00 a.m. I would say, look, I think what we saw coming out of 2025 was just a lot of momentum, a lot of demand for high-performance compute and really an environment where it favors strong product cycles and deep customer relationships. So 2026, if I think about the first few months, it is shaping up to be, again, a very exciting year. We're very excited about the data center business, the overall growth potentials. We're launching MI450. It's looking really good this year.

We have some great customer -- deep customer relationships to talk about there. And then frankly, we see just a tremendous demand for traditional compute as well. So if you look at the CPU cycle, we've always believed that the computing stack is heterogeneous and you're going to need CPUs and GPUs and FPGAs and all of these components. And that's really coming to fruition here in 2026. So a few months ago, we had our Financial Analyst Day. We put out an ambitious financial model to grow sort of 35% CAGR over sort of the next 3-, 4-, 5-year period. And I think as we look at the market dynamics, as we look at the product dynamics, I think we are very much on track to that and with an ambitious target of over $20 of earnings per share in that time frame. So lots to be excited about.

Great. Well, thank you for that. Maybe the big news last week, the deal that you signed with Meta. Maybe give us a little bit of an overview of that deal first.

Yes, absolutely. Very excited about the deepening our strategic relationship with Meta. If you think about what it really takes to build long-term lasting partnerships, it's really about road map alignment, technology alignment and aligning sort of our capabilities with what the customer is trying to achieve. Meta is a customer that we've had a long-term relationship with. They've been a deep user of CPUs throughout their data center portfolio. They've also been early adopters of MI300 and MI350 series. But what we wanted to do at this point is we actually see an inflection point in AI infrastructure.

What we're seeing is the world is much more complicated. Frankly, there is a much more workload specification. So workloads, whether you're talking about training or inference or large models or media models, you need different types of compute. And so we were looking for a way to really turbocharge, strategically deepen our relationship with Meta, and that's what we announced a few weeks ago, really a 6-gigawatt long-term strategic partnership where we're actually doing a semi-custom GPU for Meta along with all of the rest of the work that we do with them on CPUs and other parts of the system.

But it was really a vertically integrated discussion in the sense that we started from the workload first and then work through what is Meta trying to achieve with their workloads, what do they see the future of their data center infrastructure look like and then using our very flexible architecture to design something specifically tailored to their needs, which really allows us to increase our footprint in the Meta ecosystem. So very excited about that. I think as a long-term strategic partnership, it enables us to really build on each generation and frankly, get even more tailored to where the workloads are going in the future.

Thank you. And obviously, a great deal, a lot of enthusiasm for it. There have been some questions around the warrants. Can you talk about the warrants that you issued and how those warrants unlock value for you guys?

Yes, absolutely. So the way to think about it is, as I said, the AI infrastructure ecosystem is at an inflection point where deep partnerships really make a difference. And I have to say we have a lot of customers that we work with very deeply across CPUs and GPUs, and most of them don't get warrants. Warrants are a very special instrument that we use for what I would say are transformational partnerships. So when we look at Meta, what we see is a company that truly has sort of the view of the application stack. They are a foundational model builder.

They are betting big in infrastructure. And there's an opportunity not just as a consumer of chips. I mean, obviously, we'd like people to buy chips. We're talking about triple-digit billion dollar deals. Those are great things. But we actually see an opportunity to go much broader than that in the sense that you're actually charting the path for where AI infrastructure is going in the future. So the value that's accrued to AMD of a deal like this is, yes, we get to accelerate purchases, which is a great thing. But we also get to accelerate our technology ecosystem, our software ecosystem that accrues benefits beyond just the work that we're doing with Meta, but really to the overall AMD ecosystem. And the key with how we've designed these warrants is they're very, very performance-based.

So in some sense, both companies are incented to help each other win. We win when Meta's foundational models are super successful and they need lots and lots of chips. So we are motivated to give them the best infrastructure for their workloads. And they're motivated to ensure that our ecosystem is as strong as it can be, and there's a very good win-win synergistic partnership. But it is a special thing. I think the conversation is around we want to build a very rich ecosystem.

The AI infrastructure world is growing by leaps and bounds, and we have an opportunity to significantly accelerate and align with one of the strongest model builders in the ecosystem as well as our deep partnership with OpenAI, very similar from the standpoint of -- we think the model builders that are driving foundational models going forward have the opportunity to significantly align our road map with that really benefits the overall AMD ecosystem across all customers.

Okay. It's great. So those 2 being special relationships, do you anticipate that other customers for MI455 would have a similar warrant structure?

I don't. I mean you should expect that there are lots of other customers interested in MI450 and MI455. They are great products. I want to make sure that we start with the foundation of -- at the end of the day, the product has to be extremely competitive and frankly, leadership for anyone to spend gigawatts of power on our systems. But what we have is, again, lots of great partnerships across the board. I think we have a very, very competitive road map. We're excited about where MI450 is positioned when we look at the landscape.

We've always been very optimized for inference. You're now seeing the growth of inference exceed training, which is what we all expected, but that's a great thing because that means people are actually using the -- all of these models to now do real work. We're seeing the growth of agentic AI. All of these things favor our architecture. And so when I look at our combination of CPU, GPUs, networking, rack scale systems, we really have all of these pieces coming together. So lots of excitement around MI450, but I would say that the relationships with OpenAI and Meta are pretty special in how they are framed as multigenerational partnerships.

Great. Well, thank you for the overview of that deal. Maybe we could delve a little bit more into your AI products. Starting with the foundation that you had, you've done really well with MI300, MI350. Those have been leadership products that have gotten you to several billion per quarter now, over $2 billion per quarter. But now you're doing big investments into rack-scale Helios, the ZT acquisition. What's different between where you've been and where you're going within this?

Well, I think we've been -- we've made a lot of progress in the data center AI space. I think with each generation, we really increased the capability and the set of workloads that we address. I think MI300 and MI350 were great opportunities for us to really optimize, let's call it, infrastructure for inference. I think our inference capacity and capability has been really exceptional, and we've seen that adoption.

We've also been very focused on building a software ecosystem. So the idea of we want to make it super simple for people to adopt AMD technologies. And we've gone from, let's call it, in the early stages of MI300, it might have taken a number of months for customers to optimize to AMD. And now we're at the point where you can do that in a short number of days. So the tools are that good. I think the libraries are that good.

And frankly, we're using AI extensively in that ecosystem building. And when you go forward to MI450, that's why this year is so exciting for us. It really is a huge step function in capability. It's something that we planned. So we acquired ZT Systems because we believe that the rack-scale infrastructure, the whole goal, if I think about the large investments that it takes in AI infrastructure is to get our systems in the hands of users running workloads in as short a time as possible.

So it's really time to workload. And for that, the more we can do for the customers in terms of the full solution building, the easier it is for customers to deploy. So that's what we've done with the MI450 series. We've taken our view that an open ecosystem is a good thing. So the Helios rack is actually based on a standard that we developed jointly with Meta. And what that allows us to do is really leverage that entire rack-scale system.

The ZT team has been very, very active from day 1 as they joined AMD, really building that rack-scale system infrastructure. And when we look at MI450 today and the progress that we're making, it's just looking really, really good in the labs and running lots of workloads and working very closely with our lead customers.

Yes. You've made some really interesting investments into rack-scale that I know ASIC competitors, for example, aren't going to be able to make. You've laid that foundation for MI455. I guess can you just give us an update? You talked about it working well in the lab. You've talked about revenue in Q3, a bigger volume ramp in Q4. I know your competitor started building racks and there were challenges in the beginning, it took longer than they thought. What's your confidence in sort of the ability to have silicon out on time and then meet those time lines?

Yes. Well, we should start with -- these are very complex systems. So I will be clear with that, Joe. But I think we've done all of the planning and a lot of, let's call it, risk mitigation in terms of building the rack scale system. So even before we had final silicon in place, we were validating the rack-scale system. We've had a significant amount of cycles that are now being run. We've learned a lot from the ecosystem. Frankly, our partners have also been very helpful in sort of some of the early teething pains with rack-scale systems have given us a lot of feedback.

And we've designed Helios with some of the, let's call it, the previous issues in mind so that we do think it is going to come up smoothly. No question that we have a lot of work to do but we feel very good about sort of the steps that we've taken. And the most important thing is to be running workloads on these systems, and that's really what we're doing now. And so we feel good about our positioning. I think we have all the pieces in place. I think we have a strong set of relationships throughout the ecosystem to ensure that Helios ramp goes very smoothly.

And you've talked about this as a leadership product. Is that really -- is that leadership everywhere, leadership in training, leadership in inference. It doesn't seem like you're attacking some segment of the market that your competitor isn't, you're really going right at the center of the market.

Well, look, the MI450 series is a very general purpose capability. I think the way we've designed it, because of our chiplet architecture, it is quite special in how we put it together. What chiplets allow you to do is optimize for different workloads. So if you look at our standard products, we've always had an advantage in memory and memory bandwidth. I think we're going to continue to do that. Those are very, very important when you're talking about large-scale distributed inference and capabilities there.

We've also, in the same family, designed a HPC specific part, so our MI430 series. And the reason I mentioned that is there are going to be these workloads that require different data formats and different capabilities. Because of our chiplet architecture, we can fundamentally mix and match different components that allow us with, let's call it, very incremental work to get very significant workload benefit.

And then with Meta, we did -- we took that to another level to do customer-specific optimization. But we're bullish. I mean we're very bullish about the positioning of MI450. I think it's the right time, it's the right product. We have the customers who are anxious to get it in their data centers. We're now planning, as you can imagine, when you're planning multi-gigawatt deployments, we have to be planning together with the data center build-outs that are happening. And it's exciting to see all that come together.

And how do you think about the positioning versus custom silicon versus ASICs? You talked about some of the customization capabilities you can provide. But it's also -- it seems to me that this is not [indiscernible] We're all sort of focusing on the same types of workloads. So I don't know that we have the same role for ASIC customization than we've had in the past. And yet, the 2 customers that you have big deals with have deals with NVIDIA, have deals with ASIC, have deals with AMD. How do you see all that interrelating with you?

Yes. It's a very good question, Joe. And maybe we can take a minute to kind of break it up in a couple of pieces. So let's start with the workloads. I think what we're seeing in the market and what is clearly the next phase of AI infrastructure is there is like no one chip that does everything the best. It is a heterogeneous world out there. There's actually a continuum of capability going from, let's call it, the largest training clusters to inference to more specific inference workloads to even breaking up the workloads.

And I think this is a natural evolution. When you get into high-volume running AI workload, you want it to be as efficient as possible. And that efficiency comes from performance, but it's also performance per watt, it's also performance per watt per dollar. And so at the volumes that these hyperscalers are running at or these large foundational model companies are running at, you're going to want to do that optimization.

I think what we've always believed is that in that continuum, our portfolio plays really, really well. We're seeing significant CPU demand, frankly, as a result of the inference demand picking up. We're seeing significant demand for our standard product, but we're also seeing this continuum where we can do customizations for specific workloads. And frankly, I think there is always a place for ASICs as well for some more tailored applications. The key is how do we get we want to get for the best of both worlds, right? You want to be able to have flexibility and time to market. That's what we believe our chiplet ecosystem does and our overall ecosystems investments do.

But you also want to be able to tailor for specific workloads. And so that's kind of why we really believe that this world is going to come to a place where you do have different chips that are being optimized for different workloads and the capability that allows you to optimize the quickest where you get, let's call it, maybe not full tailoring or full ASIC, but you're able to get, let's call it, 80%, 90% of the benefit at a shorter time with similar economics is a great thing.

Great. Can you talk about the systems level things that you need to provide? I mean in networking, you're scaling up with UALink, but there's sort of UA link through Ethernet. There's a CPO migration to think about. Can you just kind of talk about your networking road map? And how important is that to the AMD rack-scale road map going forward?

Yes. No question, networking road map is very, very important. What we're trying to unlock is systems performance and systems performance includes all the elements of compute as well as the networking infrastructure to scale up and scale out. I think we have a great team internally part of the acquisition of Pensando that we did. We've done quite a bit of work on understanding the networking workloads. We have our own scale-up NIC as well. We work across the ecosystem in terms of some of the switching partners.

I think the key for this is, again, to -- from our standpoint, it's about open standards and it's about giving the customer choice. So I think UALink is a very specific AI optimized network that we believe can be beneficial. We also believe that there's a large set of customers who gravitate towards Ethernet because of its compatibility. And so we support UALink over Ethernet. We'll continue to support that Ethernet ecosystem. And the key for us is to be very mindful about rack-scale infrastructure performance and capability. Lots of optimization on both the hardware and software ecosystem. I think we're deeply partnered across the ecosystem to deliver that rack-scale performance.

Great. And then before you had the Meta deal, you had the OpenAI deal, which was the same basic size, 6 gigawatts. Everything the same with that deal. I know NVIDIA has kind of moved from a little bit more focus on provisioning their own data centers versus what you're doing, which is more cloud-centric. Just is that OpenAI deal tracking to what you thought it would?

Well, I have to say, first of all, I think our relationship with OpenAI is better than it has ever been. I think our strategic relationship was definitely we're much more tied in terms of road map. We're actively planning what are the installations of the first gigawatt of capability, and it's really playing out as we expected.

So I would say nothing has changed with the overall deal structure. I would say that I am quite, quite pleased. It's actually clearly paying off in terms of the technical alignment that we have, the prework that we're doing across the MI450. We are basically co-validating together. We're planning those installations together. So yes, we feel great about that relationship.

Okay. Great. You've talked about this is a trillion-dollar market end of the decade, and you've talked about $120 billion of AI revenue for AMD. I guess the market seems to be concerned about the sustainability of the strength we're seeing now and people look at the hyperscale cash flows as being sort of neutral to negative and the markets kind of understands that things are strong near term, but worried about the duration. What's your view on that? Why you continue to believe in trillion-dollars we talked backstage. There's a lot of indications of that. What gives you the confidence in the sizing of that market?

Yes. Look, we feel really good about the market, the build-out, sort of the most important thing when you're sitting on my side is to make sure that as the infrastructure is being deployed, it's solving real-world problems. And that's what we see. I mean we see that the investment in AI infrastructure, in some sense, we're equating that investment with productivity and intelligence -- and that's a great thing.

So yes, we are all investing ahead of the curve, but well within the reason of where we think the payoff is going to be. I can just tell you like every week, every month, we are seeing significant new enterprise use cases that are showing the payoff of what AI can give us. And as I talk to enterprise customers, like we're still in the very early innings of deployment. So all of the infrastructure that we are building out, and really, these are planned builds, right? So if you think about CapEx discussions today are planned builds later in '26, '27 and beyond, they're really to address that enterprise demand and really delivering the payoff of AI.

We are seeing it. We are seeing the early signs of it. We're seeing the early signs of it in our own business. We're seeing the early signs of it in our customers' businesses. And I think the thing that's a little bit different, Joe, which maybe people need to understand is it's just not all about GPUs. Like this is not just about deploying accelerators. This is actually about deploying the entire compute infrastructure you need to service all of those agents that we're all going to be spawning with our new AI capabilities, right?

So if you think if a company has 10,000 people and they add another 10,000 agents on top of that, they're going to need a lot more compute to satisfy what all of those agents are doing. And we're seeing that. We're seeing -- actually, as much as I'm very, very excited about the GPU portion of the business. I mean the CPU portion of the business has actually far exceeded my expectations in terms of demand. And I was pretty bullish to begin with.

We talked about -- I talked about like a high teens CAGR in the compute market at our Analyst Day. And I can tell you that every indication that I'm seeing today is that, that compute market is even much larger than that. The ratio of traditional compute to accelerated compute is such that you need really a very balanced system overall.

Yes. That seems like we've moved from theory to seeing that play out in real time now with that.

Did you not believe me when I said that, Joe?

I believed you, but we certainly are seeing evidence of it now. Can you talk about that? And it seems like the microprocessor market is dealing with shortages at the moment. What's your visibility into being able to meet that demand that's out there?

Well, first of all, we have a very strong road map. I think we have executed very well. As we ramped Turin, it was a very, very fast ramp. And with each generation of our EPYC processors, we've actually increased the workload coverage. So we started with, let's call it, the main cloud workloads at the hyperscalers. But we've now really expanded to the breadth and depth that you would expect with strong products, and that's across both hyperscalers as well as enterprise.

I think what we're seeing is really that build-out continue and continue in a very positive fashion. So back to your comment about are there -- is it supply tightness? Yes, there is supply tightness, but that's really because the market sizing is bigger than what we had forecasted 3 or 6 months ago. And so it always takes time for the supply chain to catch up with what the market wants. I can say that we are very, very well positioned from a supply standpoint to meet a large percentage of that demand. We are still working very closely with our supply chain partners to expand that capability as we go through '26 and '27.

And what we're looking for is, again, durable demand that is not just, hey, are we just catching up because we haven't upgraded CPUs? No. I mean that is the wrong way to look at it. Yes, we are upgrading. You did say that, too, didn't you? No. But look, I mean, I think we never know until we actually see what happens in the workload. I would say even the hyperscalers are surprised. So if you talk to our top customers, they're like, wow, Lisa, like the demand for CPU compute sitting along AI was perhaps something that was under forecasted.

And so we are in the process of catching up. But I think it's a great time. It's a great time because, one, we were already, from an AMD standpoint, expanding our workload coverage. And then two, you're seeing the customer demand really strengthen as well. So we will continue to increase our supply coverage as we go through this year and into next year, but this definitely feels like a very durable cycle, and it's a very pleasant -- I won't say a surprise, but it's a pleasant development as we think about our overall goal is to provide the right compute for the right workload. I think our data center business clearly shows that we have all the right pieces for this AI cycle.

And I know Forrest had talked recently about sort of Turin versus Granite Rapids is as close as this is going to get and Venice is a clear indication of AMD pulling ahead. Can you talk about that? And I feel like I guess your competitor would say, we have fabs, we can meet this demand, whereas AMD may be constrained on wafers. Any concern about that?

Well, I would say from a competitive standpoint, we feel really good about Venice. I think we were -- we continue to be very aggressive with each generation of our CPU build-out. We continue to broaden the workloads that we're covering. Venice was one of the very, very first products in TSMC 2-nanometer using our chiplet architecture. It is on track to ramp very nicely in the second half of the year. I think we feel very good about our ability to expand to the demand out there.

I would say what we're seeing about Venice, which tells you a little bit about the competitive position is each generation, what we're trying to do is align customer ramps with our ramp, right? We want customers to have the best technology that they can. And frankly, -- that doesn't always happen because customers have their own cycles that they're going through. With Venice, like every one of our large customers want Venice the moment it comes out. And that kind of gives you a sense of how good it is because if you have power to spend, you want to spend your power on the best technology out there. And that's what Venice will be when it comes out.

Great. And then on the CPU side, can you talk about competition within ARM? Your bigger hyperscale customers do have some ARM deployments that are out there. Just how do you see them fitting into this ecosystem?

Well, look, I think ARM has always been a part of the data center ecosystem. I would say it tends to be on the lower performance side of it. We view it as not about ARM versus x86. We view it as you want the right processor for the right workload. And so the performance per watt capabilities, performance per dollar capabilities, the overall TCO are what's critical. And we think with the broad coverage that we have as we go into the Venice generation, I see our TAM expanding, and I see our share expanding because of the capabilities of Venice.

Okay. Great. So I have one more question, and I'll turn it to the audience. The role of memory in all of this, are you seeing impact of memory shortages on the GPU side, on the CPU side, any part of your business?

Yes, it's a dynamic world right now. I think when you look at the memory market, first of all, we plan with the memory vendors many years in advance. So we've been planning for the MI450 ramp. We're planning our HBM4 ramp across the memory ecosystem. So we feel good about where we're positioned from an HBM standpoint. There are other knock-on effects on the memory market right now. Certainly, if you talk to any of the memory vendors in terms of where DDR4 and DDR5 are positioned as well as some of the consumer grades.

The impact that we're seeing is certainly the memory prices are affecting system prices. So you see system prices going up. I will say that the enterprise demand on the data center side seems, again, very durable. I think people are wanting the compute, needing the compute. So although they're paying a bit more than they might have 6 or 9 months ago, I think that is the main impact. I am watching the impact on the PC market. So we would expect that there might be more sort of cost pressures.

And as those cost pressures, they may change a little bit the PC market dynamics. So whereas our overall sell-through in the PC market is actually quite good. We are expecting that in the second half of the year, we may see a more muted part of the market just as memory prices are volatile. But we'll have to see how it plays out. I mean at the end of the day, -- the one thing about the industry is we tend to like demand, and we tend to like fulfilling that demand. So I think there is a lot to still play out. But on the data center side, it is full speed ahead, and we'll have to see what happens in the consumer markets.

Very helpful. Let me see if we have any questions from the audience.

Following OpenAI and Meta, can you just talk about the propensity for other kind of gigawatt scale deals with other hyperscalers, AI lab types?

Yes. Absolutely. So look, we are very ambitious with what we can do in the data center AI market. I think from a road map standpoint, as much as we're excited about the MI450 series, we're actually super excited about what's beyond as well. These days, I think there are multiple gigawatt scale customers. I think every lab is looking for choice at this point. The -- if you -- this idea of diversity of compute is important.

So with Meta and OpenAI, I think we've built foundational multi-generational deals that will absolutely help pull the entire ecosystem and enhance the entire AMD ecosystem. I think there are a number of other customers that are, let's call it, in that scale that we see as very strongly interested in MI450 and beyond. Back to this comment of what are we trying to do. The -- if you think about the ambition that we have in the data center AI segment, it is a very, very large TAM, and we are currently at the very early stages of building out our business. What we're trying to do is accelerate it. So we've talked about growing over 80% CAGR over the next 3 to 5 years in our data center AI segment. I think with the visibility that we have with some of these large deals as well as the other -- the broader customer set, I think we have a very good confidence to not only meet those but exceed those targets as we go forward.

Can you comment on the status of the Chinese market opportunity and also maybe also competition from China?

Sure. So we've always stated that the Chinese market is an important market to us. We are broadly -- we have a broad set of customers in the Chinese market on CPUs in other areas. On the GPU side, it is still a little bit complicated. We were able to ship some MI308s last quarter in the fourth quarter that we reported, and we talked about approximately $100 million this quarter. We're in the process of applying for licenses for the next generation of the MI325 chips.

I think the Commerce Department and the U.S. government are still going through the approval processes for that. So it's very, very hard to predict. And for that reason, we're not forecasting additional revenue going forward. We would certainly like to satisfy our customers in China. I think there's a lot that we learned by participating in the Chinese market because they also have a set of models that are somewhat different than the U.S. models, and we want to be able to service that, but we'll have to see how that whole environment plays out from a licensing standpoint over the next couple of months.

And do those limitations kickstart Chinese competition?

Well, I think Chinese competition was always going to be fierce. So we should expect that in a world as competitive as AI, we have to give the Chinese chip providers credit for what they're able to achieve. That being said, I think the road maps that we have from a U.S. technology standpoint are very, very strong. We want to be able to participate in the global market, and we need to continue to work with both governments to enable that to happen.

Last question.

There seems to be a debate out there as to whether you're able to ship rack-scale solutions in volume in the second half of this year? Or do you have enough -- I mean, you have enough CoWoS capacity to do that? Or is this much more of a 2027 story?

We definitely have enough CoWoS capacity. I know that there's lots of people trying to check various things. The best thing I can tell you is we have the capacity. We have the technology. We have the deep customer relationships. We have the data center providers have allocated space for it. So we have to execute that ramp.

And we've always said the ramp is second half weighted. Think about it a little bit in Q3, but really ramping sharply as we get into Q4. This is no question, a very, very important ramp for us. It's something that we've been planning for, for many quarters, and we feel good about the ramp.

Great. Well, we'll wrap it up there, Lisa. Congratulations on everything you achieved, and thank you so much for being here today.

Thank you.

Greetings, and welcome to the AMD Conference Call. [Operator Instructions] As a reminder, this conference is being recorded.

I would now like to turn the conference over to your host, Matt Ramsay, Vice President, Financial Strategy and Investor Relations. Thank you. You may begin.

Good morning. Thank you for joining on such short notice, and welcome to our call to discuss the significant new AI partnership between AMD and Meta. By now, you should have had the opportunity to review a copy of our press release and our Form 8-K filing discussing this announcement. If you have not had the opportunity to review these materials, they can be found on the Investor Relations page of amd.com.

Participants on today's conference call are Dr. Lisa Su, our Chair and CEO; and Jean Hu, our Executive Vice President, CFO and Treasurer. This is a live call and will be replayed via webcast on our website.

Today's discussion may contain forward-looking statements based on our current beliefs, assumptions and expectations, speak only as of today and as such, involve risks and uncertainties that could cause actual results to differ materially from our expectations. Please refer to the cautionary statement in our press release for more information on the factors that could cause these actual results to differ materially.

With that, I'll hand the call to Lisa.

Great. Thank you, Matt, and good morning, and thank you all for joining the call today. We are announcing a significant expansion of our strategic partnership with Meta, including a new multi-year multigeneration agreement that positions AMD at the core of their next-generation AI infrastructure.

2025 was a defining year for AMD with record results across the business, and we're carrying that momentum into 2026. AI demand is accelerating rapidly as customers scale modern AI infrastructure across both accelerated and general-purpose compute. Through the leadership technology and consistent execution, we have built one of the premier data center AI franchises in the industry, anchored by our differentiated Instinct GPU road map and our leadership EPYC CPU portfolio.

AMD is uniquely positioned to deliver high-performance, energy-efficient compute across the full spectrum of AI workloads. Meta has been a close partner over multiple generations, deploying millions of EPYC CPUs and hundreds of megawatts of MI300 and MI350 Series GPUs across their global infrastructure. Meta was also an early definition customer for our MI450 series, and we developed our Helios rack-scale architecture on the OCP Open Rack Wide standard in collaboration with Meta.

Today, we are significantly expanding our relationship. Under this agreement, Meta is expected to deploy 6 gigawatts of AMD Instinct GPUs across multiple product generations. To meet their evolving AI requirements, we are co-engineering a custom GPU accelerator based on our MI450 architecture optimized specifically for Meta's workloads. Initial shipments supporting the first gigawatt deployment are scheduled to begin in the second half of 2026 and will leverage our Helios rack-scale architecture with the custom MI450-based Instinct GPU and our 6th Gen EPYC CPU code named Venice.

This partnership firmly establishes AMD at the center of one of the industry's most significant AI infrastructure deployments and highlights the strength of our end-to-end platform strategy. As AI workloads scale, customers are increasingly looking for solutions tailored to their specific architectures and performance requirements. AMD's leadership in chiplet design and advanced packaging is a key differentiator and enables us to rapidly leverage core building blocks of our AI platform and tailor them for the optimal compute, memory and networking needs of specific customer workloads.

The custom MI450-based GPU we are developing with Meta is a direct result of this capability, delivering workload-specific optimizations while leveraging the MI450 platform, the Helios rack-scale system infrastructure and our open ROCm software ecosystem, giving Meta the advantages of a custom solution with the benefits of the broader MI450 ecosystem and GPU programmability.

As these platforms deploy at gigawatt scale, the ecosystem optimizations across ROCm, AI frameworks and system software will extend well beyond this engagement, strengthening our broader Instinct franchise and expanding opportunities across our entire customer base.

In addition to expanding our GPU engagement with Meta, we are further deepening our EPYC CPU partnership. We are seeing accelerating CPU compute demand driven by the rapid scaling of AI infrastructure across model development, inferencing, data processing and the rise of agentic AI. As deployments grow in scale and complexity, CPUs remain a strategic foundation of the compute stack, driving efficiency, orchestration and system-level performance. EPYC is well positioned to capture outsized value in this next phase of AI expansion.

Meta is already a multi-generation EPYC customer with EPYC processors powering the majority of core services across their global data center footprint. Building on our deep road map alignment, Meta will be a lead customer for our 6th Gen EPYC Venice processor at launch later this year.

We have also partnered closely on a new addition to our Zen 6 family code named Verano, incorporating workload-specific optimizations to deliver leadership performance per watt and compelling TCO. The expansion of our partnership across GPUs and CPUs is another strong proof point that the world's most ambitious AI builders are choosing AMD Instinct and EPYC platforms as the foundation of their AI infrastructure. I want to thank Mark and the entire Meta team for their collaboration and partnership. We are extremely proud to work together to advance the future of AI at scale.

Importantly, this engagement reflects the strong and growing demand we are seeing for our MI450 Series and Helios architecture. Overall, there is significant excitement in the market for the MI450 Series and Helios. And from an execution standpoint, we are making excellent progress. MI450 and Helios are currently in hardware and software validation, running the latest inference and training workloads.

We are working closely with our lead customers, supply chain and ecosystem partners to ensure a smooth ramp. We expect to begin customer sampling shortly and remain on track to begin production shipments of both the standard MI450 series and the custom MI450-based CPU for Meta in the second half of 2026.

Now I'll turn the call over to Jean to provide additional details on the agreement.

Thank you, Lisa. Today's announcement of a 6-gigawatt agreement with Meta is another significant step in scaling our data center AI business, consistent with the ambitious plan we set out at our Financial Analyst Day.

Let me provide some context on the financials. The Meta deployment is expected to generate data center AI revenue of significant double-digit billions of dollars per gigawatt. Revenue will begin in the second half of 2026 and ramp alongside our MI450 deployment with other customers.

As part of the agreement, to further align strategic interests of both companies, AMD has issued Meta a warrant for up to 160 million shares of AMD common stock. The warrant is performance-based. The first tranche vests with the initial 1-gigawatt of shipments of AMD Instinct GPU, with additional tranches vesting as Meta's purchases of Instinct GPUs to scale to 6 gigawatts. Vesting is further tied to AMD, achieving certain stock price thresholds with the final tranches vesting at a price of $600 per share.

In addition, exercise of the warrant is tied to Meta achieving key technical and commercial milestones. The unique structure aligns Meta and AMD, driving significant long-term revenue growth and is accretive to our non-GAAP earnings per share, while enabling Meta to share directly in the upside of our mutual success. This partnership marks another significant step forward in delivering our ambitious long-term financial model, including greater than 80% CAGR of our data center AI business and generating more than $20 in annual earnings per share within the next 3 to 5 years.

Let me now turn the call back to Lisa.

Thank you, Jean. Let me close by saying that today represents another major milestone for AMD's AI strategy. Through this multiyear multi-generation agreement to deploy 6 gigawatts of AMD Instinct GPUs, we are significantly expanding our partnership with Meta, broadening our AI footprint and deepening our co-development and road map alignment with one of the world's leading AI companies that is building at massive scale.

We believe that the scale of this deployment and the ecosystem benefits that it drives will further strengthen our AI platforms and expand opportunities across both existing and new customers. The current AI infrastructure build-out is one of the most significant technology investment cycles in decades. The expanded partnership with Meta, together with our previously announced partnerships with OpenAI, Oracle and others, demonstrates the strength of our multi-generation Instinct and Helios road maps and firmly establishes AMD at the center of this next phase of AI growth.

From silicon to systems to software, we are executing with scale, speed and discipline, leveraging our differentiated end-to-end portfolio and deep strategic partnerships to capture the AI opportunity, accelerate sustained data center growth and deliver long-term shareholder value.

Now I'll turn the call back to Matt for Q&A.

Thank you, Lisa. Thank you, Jean. For today's Q&A session, please focus your questions on today's announcement. [Operator Instructions]

Thank you very much. Operator, you can please poll for the first question.

[Operator Instructions] Our first question comes from the line of Joshua Buchalter with TD Cowen.

Congrats on the deal. For my first one, I just wanted to hit one that's gotten in my inbox a couple of times. It seems like it wouldn't be the case since Meta doesn't have a third-party cloud business like the other hyperscalers, but could you confirm whether or not there's any overlap between this deal and the OpenAI deal you announced a few months ago? And are there any details you're able to share on how the 6 gigawatts compares to the OpenAI deal from a timing and value standpoint?

Yes, absolutely, Josh. So thanks for the question. If I give you the overall context, look, I think Meta's plans are extremely ambitious in terms of what they're trying to do in terms of AI infrastructure build-out. We are super excited about this partnership. Meta has been a long-standing partner of AMD over the last few generations, but this agreement really takes our relationship to the next level and significantly expands on what we were doing before.

In terms of your question about whether there's any overlap, no, there's no overlap from the standpoint that Meta's -- this agreement is really for Meta's workloads. Our -- the custom GPU that we're building is optimized for Meta's workloads. It's the first time we are doing a custom GPU of this style using our MI450-based architecture.

So I think, look, both OpenAI and Meta are incredible companies. They're doing tremendous innovation, and we are super happy to be deeply partnered with them. And what we are really trying to do, which we've always been is if you think about the AI space, there are lots of different workloads and workloads of different types. And what we want to do is partner deeply so that we're providing the technology that each company needs to satisfy their ambitions.

Matt, are we doing follow-ups?

No, we can just try to keep this to one question per caller given we got a [indiscernible] call. But thank you, Josh. Operator, the next caller please.

Our next question comes from the line of Vivek Arya with Bank of America.

Lisa, I had a more fundamental question, which is where is the economic value add here? You're giving away $30 billion roughly of value in your stock. And in return, you're getting about $30 billion, $35 billion of net income. So it seems like an even swap. So where is the value add here? So if I ask it in a different way, if the product is so good, why does AMD need to give up 10% of your equity? And is now every MI450 customer going to ask for this kind of a deal?

Yes, sure, Vivek. So let me -- let me start with some context. If you look at the context of these partnerships, every deal of this scale is very unique. We -- as Jean said, this deal is very accretive to AMD's earnings. So it is a very good deal for AMD shareholders. But what we're setting out to create is a strategic partnership where we're going much, much deeper together. And I view this as a very transformational partnership from an AMD standpoint. Meta is operating at gigawatt scale. We are actually working with them deeply in terms of their technology road map, and we're working together on hardware, software systems.

And with that, it accrues significant value to our overall road map going forward. And I think you have to look beyond just these particular economics, which, as I said, are very accretive to our earnings, but you also have to look at what it's doing across our entire road map and our future road map going forward.

So if you look at the structure of our warrants in this case, is -- again, it's a very aligned incentive structure. Meta is making a big bet on deploying at large scale for AMD, which is great. AMD benefits from this large-scale deployment, which brings revenue scale, ecosystem maturity, software maturity. And assuming that we satisfy all of the purchases as well as the share price thresholds, AMD shareholders will benefit significantly, and Meta gets to benefit as part of that.

So to your question, no, I don't think every MI450 customer is of this size and scale. Our standpoint is we are looking for defining partnerships for our AI franchise, and Meta is one of those defining partnerships.

Our next question comes from the line of Timothy Arcuri with UBS.

Lisa, I wanted to ask a question along the same line. So it sounds like the exact same deal that OpenAI got right down to the same kind of stock prices as well. So it is a lot of revenue. But speaking to the precedent here, would Meta have done this without this deal. So when they saw the deal from OpenAI, did they say, okay, I'll do a deal if I get the same deal. So I guess speaking to the precedent if you're going to sign with Amazon over somebody else, does this sort of open up the discussion that they would want the same kind of deal.

Tim, I think the way -- if I give you some sort of background on how we came up with this -- this wasn't about, hey, are we trying to do something similar with other customers. It's not like that.

What it is about is Meta has been a tremendous partner for AMD over the last several generations, and we appreciate that, by the way. I mean they have been a big adopter of EPYC. They were an early adopter of MI300 and MI350. But without this strategic agreement, I think we would have done well. I think MI450 would have done well.

But what we are looking to do is do something transformational. And when you talk about gigawatt scale deployments and 6 gigawatts over 5 years, that is transformational in terms of where we see our business. And in addition to that, they're at the forefront of what's happening with models and model builders, they are optimizing workloads for their future, and we are optimized alongside with them.

So I think if you look in that context, they are different deals, but they are very important strategic deals in terms of the shape of AI going forward. And if you think about this market, there's probably only a handful of companies that are deploying at this scale. And so to have Meta today and OpenAI as well as strategic partners that anchor AMD's AI strategy, I think, is a really, really good place for us to be.

Our next question comes from the line of Blayne Curtis with Jefferies.

Congrats. Lisa, I just wanted to kind of dive in a little bit more on the economics. Should we think about this deal, you've previously talked about a range of like dollars per gigawatt and maybe OpenAI was at the lower end of that. Is that the same kind of economics? And then I'm just kind of curious in terms of support of these 2 deals, how do you think about the OpEx part of the equation?

Yes. So the way I would say it is we've talked about -- I think Jean might have mentioned that when we think about the revenue from, let's call it, the GPUs, we're talking about something like double-digit billions per gigawatt. And so that is the range that we're talking about. When you -- I'm sorry, the second part of your question?

OpEx [indiscernible]...

OpEx consideration to support of these 2 deal?

Yes, Blayne, on the OpEx side, because it is based on MI450, you can think about it's just another variant of MI450. So the incremental OpEx actually is quite minimal. That drives significant operating model leverage.

Our next question comes from the line of Aaron Rakers with Wells Fargo.

Yes. In the prepared comments, obviously, this is all about the Instinct GPU road map, but you've also emphasized the importance that the CPU plays in these architecture deployments going forward, particularly probably around AI inferencing.

I'm curious, Lisa, if you could talk a little bit about how you see the evolving competitive landscape in CPUs, in particular, one of your key competitors trying to push Arm-based architecture more prolifically on a stand-alone basis. I'm curious how you see that playing out in these architecture deployments.

Sure, Aaron. So thanks for the question. I think what we've said and what we said on the last earnings call and the last couple of earnings calls is the CPU market is absolutely on fire. I mean there is significant demand. It has continued to grow, and it really is a result of the AI infrastructure deployments as inferencing scales, as agentic AI scales. And our EPYC portfolio is in an extremely good position. I mean everything that we see, certainly with our current Zen 5 class [ Trent ] products, we are very widely deployed in AI infrastructure.

And as we go into our Zen 6 family with Venice and the addition of Verano, we actually see our workload coverage increasing across a number of our largest customers. So I think it's great that there's so much interest around CPUs. I think that is something that we've always believed that you need all different types of compute. And we feel we're very, very well positioned from a competitive standpoint.

Our next question comes from the line of Tom O'Malley with Barclays.

Congrats on the deal. Lisa, I wanted to dive in on the custom commentary from the release and then also in your answer to one of the questions earlier. What does it mean specifically about a custom design? Is it just a different flavor of a tape-out? Is the system architecture going to look a little bit different? And then in the future, are you going to be doing more custom-style tape-outs and/or systems with other customers that come on board? Just a little curious what that means specifically.

Yes, Tom, I think this is actually a pretty interesting new thing that we're doing here. Look, we've always believed that when you look at AI infrastructure, there's no one chip that does it all, especially when you look across training and inference and big models and small models and different workloads that you're trying to optimize.

So what's unique about this deal is we started with the workload first. We didn't start with the chip. We started with the workload. What is most important to Meta for their future workloads, their highest volume workloads. And then we worked back from that with our chiplet architecture. What's unique about our chiplet architecture is we have all the building block pieces, but you can put them together and configure them in different ways to give you sort of different performance and system characteristics.

And so what we've done together with Meta is we're using our chiplet architecture to come up with a new variant based on the MI450 architecture. As Jean said, it's highly leveraging the base capability, which gives us both development scale as well as, frankly, a lot of leverage as we're bringing up these technologies. But it's not just the chip optimization. I think it's chip level, board level, system level, and that comes together in a solution that I think gives Meta is the best of both worlds, which is something that is highly tuned to their workload, but takes advantage of the entire infrastructure and supply chain and everything else that we are developing for the base MI450 architecture.

So to your question of do we expect to do more? I would expect that for high-volume workloads, there will be benefits to doing something that is, let's call it, more customized in a GPU format. Note that we're not doing full ASIC, which, again, usually takes a lot more time, but we're doing something based off of our foundational architecture. So I think it's a good opportunity to expand our portfolio as customers are increasing their volumes.

Tom, just added to what Lisa said, there's no additional tape-out needed for this customer chip.

Our next question comes from the line of Antoine Chkaiban with New Street Research.

Actually, I'd like to follow up on the prior question. I'm wondering, like given this is a custom deployment, how much of Meta's software investment in ROCm is truly transferable versus custom? And maybe if I ask the question differently, does this deal really create a self-reinforcing ecosystem that makes subsequent AMD deployments progressively easier, as I imagine, custom hardware also means custom software.

Antoine, actually, it's extremely leverageable. So I don't know if I would say 100%, but 95-plus percent is the way you should think about it because all of the underlying software is using the MI450 architecture. So if you think about all of the work that we have to do in terms of the base libraries, the kernel optimizations, all of that stuff is highly leverageable to the rest of the AMD ecosystem.

And again, this is a GPU. So from a GPU standpoint, it's already highly programmable. So Meta has already been a very strong partner with us on the software ecosystem. If you look at all of their work with PyTorch and the open ecosystem. And I expect that we will continue to work closely together as we scale these deployments to gigawatts plus.

Our next question comes from the line of Mark Lipacis with Evercore ISI.

Lisa, can you help us understand what is -- what kind of visibility do you have under this deal? Like I'm trying to understand what hard orders do you have right now for the near term? And is there like a take-or-pay element on Meta's part here? Or like can they elect to opt out? Can you just help us understand like how hard are the orders? I appreciate you anticipate getting the full 6 gigawatts. But if you could help us understand the near-term visibility and the longer-term visibility.

Sure, Mark. So I think as we said, we are signing a long-term strategic agreement that goes across 5 years for 6 gigawatts. The first gigawatt is committed and will start shipments in the second half of 2026. As you know, the supply chain overall is tight, and so we're planning it very tightly together for their data center builds in terms of which data centers these things are going in.

But I think we have a very good near-term visibility. The important thing I want to mention, again, Mark, is we had already a very strong relationship with Meta. But what this agreement does is it really does take our near-term work together to the next level. And that has been very, very positive. And I really feel that it is a big addition to what we see in terms of MI450 adoption to include the custom GPU optimized for Meta's workload.

So in terms of long-term, we are actively working on beyond MI450 as well. As you know, we've already been well into the development of MI500 and beyond. And I expect with each generation, we can get even more optimized as we learn more about their workloads and they learn more about our architecture.

Very helpful.

Thanks.

Our next question comes from the line of Harsh Kumar with Piper Sandler.

I was curious about how the deal was won, especially the custom GPU part. This is not the norm for you guys. So was this sort of a bake-off against some other people? Or did Meta approach you specifically with an idea of custom chip? I'd be curious how this played out.

Harsh, I think the best way to say it is we're always in deep discussions with Meta. They are a very close partner. What has been very good about this engagement is Mark and I have spent a good amount of time together on how we really align our road map with their next-generation infrastructure. I think Mark is extremely ambitious with what he wants to do with Meta in terms of what they're doing with their models. I think they have a set of requirements -- he has a great team that works with him, and we have been working closely with them on how can we both optimize as well as expand our relationships.

So from that standpoint, we started with workload first, like what are you trying to accomplish? And we came up with ideas for how we could even enhance the base standard product of MI450 to broaden our workload adoption. So it was a very collaborative effort and one that I think talks about the types of bets that companies are making. Like we are making certainly a big bet on Meta, and I think Meta is making a big bet on AMD.

Operator, I think we have time for just one more question before we end today's call. Thank you.

Our last question today will come from the line of Srini Pajjuri with RBC Capital Markets.

Lisa, I just want to clarify, you gave us some targets at the Analyst Day last year, $20-plus EPS. Just curious if those numbers already included the Meta deal. And then you also talked about potential other customers, and obviously, Meta is one of them. I'm just curious if that $20 number includes any other future deals as well.

Yes. So going back to our financial model, I think we put some very ambitious goals out there in terms of our revenue as well as our EPS and $20 EPS. The -- when we came up with that number in November, I wouldn't say we had this Meta deal specifically baked in. This was still very, very much in the works. This does expand our relationship with Meta, which is a great thing. And when we look at these financial models, we have a broad set of customers and a broad set of customers that we are actively engaging.

So I think what this should give you is clearer visibility into how we intend to both achieve and exceed our financial model, having a great strategic partnership like Meta as well as some of the other partnerships that we have talked about. And you should imagine that the overall interest in MI450 is very high. And in addition to the partnerships that we're talking about right now, there are a number of other strategic partnerships that are underway. And I feel very good about our trajectory towards that long-term financial model.

Thank you very much, everyone. I think this is the end of the Q&A period. We really appreciate everybody jumping on, on short notice. It's an exciting day for AMD, and thank you for your interest and your time.

Operator, please go ahead and close the call. Thank you.

Thank you. This concludes today's conference call. You may disconnect your lines at this time. Thank you for your participation.

Greetings, and welcome to the AMD Fourth Quarter and Full Year 2025 Conference Call. [Operator Instructions]. And please note that this conference is being recorded.

I will now turn the conference over to Matt Ramsay, VP of Financial Strategy and IR. Thank you. You may begin.

Thank you, and welcome to AMD's Fourth Quarter and 2025 Full Year Financial Results Conference Call. By now, you should have had the opportunity to review a copy of our earnings press release and accompanying slides. If you have not had the opportunity to review these materials, they can be found on the Investor Relations page of amd.com.

Today, we will refer primarily to non-GAAP financial measures on the call. The full non-GAAP to GAAP reconciliations are available in today's press release and in the slides posted on our website. Participants in today's conference call are Dr. Lisa Su, our Chair and CEO; and Jean Hu, our Executive Vice President, CFO and Treasurer. This is a live call and will be replayed via webcast on our website.

Before we begin, I would like to note that Mark Papermaster, Executive Vice President and CTO, will present at Morgan Stanley's TMT Conference on Tuesday, March 3. Today's discussions contain forward-looking statements based on our current beliefs, assumptions and expectations, speak only as of today and as such, involve risks and uncertainties that could cause actual results to differ materially from our current expectations. Please refer to the cautionary statement in our press release for more information on factors that could cause actual results to differ materially.

With that, I will hand the call to Lisa.

Thank you, Matt, and good afternoon to all those listening today. 2025 was a defining year for AMD with record revenue, net income and free cash flow driven by broad-based demand for our high-performance computing and AI products. We ended the year with significant momentum with every part of our business performing very well. We saw demand accelerate across the data center, PC, gaming and embedded markets, launched the broadest set of leadership products in our history, gained significant server and PC processor share and rapidly scaled our Data Center AI business as Instinct and ROCm adoption increased with cloud, enterprise and AI customers.

Looking at our fourth quarter, fourth quarter revenue grew 34% year-over-year to $10.3 billion, led by record EPYC, Ryzen and Instinct processor sales. Net income increased 42% to a record $2.5 billion and free cash flow nearly doubled year-over-year to a record $2.1 billion. For the full year, revenue grew 34% to $34.6 billion, and we added more than $7.6 billion of Data Center segment and Client revenue.

Turning to our fourth quarter segment results. Data Center segment revenue increased 39% year-over-year to a record $5.4 billion, led by accelerating Instinct MI350 Series GPU deployments and server share gains. In server, adoption of fifth gen EPYC turn CPUs accelerated in the quarter, accounting for more than half of the total server revenue. Fourth gen EPYC sales were also robust as our prior generation CPUs continue to deliver superior performance and TCO compared to competitive offerings across a wide range of workloads. As a result, we had record server CPU sales to both cloud and enterprise customers in the quarter and exited the year with record share.

In cloud, hyperscaler demand was very strong as North American customers expanded deployments. EPYC-powered public cloud offerings grew significantly in the quarter with AWS, Google and others launching more than 230 new AMD instances. Hyperscalers launched more than 500 AMD-based instances in 2025, increasing the number of EPYC cloud instances more than 50% year-over-year to nearly 1,600.

In the enterprise, we are seeing a meaningful shift in EPYC adoption, driven by our leadership performance, expanded platform availability, broad software enablement and increased go-to-market programs. The leading server providers now offer more than 3,000 solutions powered by fourth and fifth gen EPYC CPUs that are optimized for all major enterprise workloads. As a result, the number of large businesses deploying EPYC on-prem more than doubled in 2025, and we exited the year with record server sell-through.

Looking ahead, server CPU demand remains very strong. Hyperscalers are expanding their infrastructure to meet growing demand for cloud services and AI while enterprises are modernizing their data centers to ensure they have the right compute required to enable new AI workflows. Against this backdrop, EPYC has become the processor of choice for the modern data center, delivering leadership performance, efficiency and TCO. Our next-generation Venice CPU extends our leadership across each of these metrics. Customer pull for Venice is very high with engagements underway to support large-scale cloud deployments and broad OEM platform availability when Venice launches later this year.

Turning to our Data Center AI business. We delivered record Instinct GPU revenue in the fourth quarter, led by the ramp of MI 350 Series shipments. We also had some revenue from MI308 sales to customers in China. Instinct adoption broadened in the quarter. Today, 8 of the top 10 AI companies use Instinct to power production workloads across a growing range of use cases. With the MI350 series, we are entering the next phase of instinct adoption, expanding our footprint with existing partners and adding new customers.

In the fourth quarter, hyperscalers expanded MI350 Series availability, leading AI companies scale their deployments to support additional workloads and multiple neocloud providers launched MI350 Series offerings that deliver on-demand access to instinct infrastructure in the cloud.

Turning to our AI software stack. We expanded the ROCm ecosystem in the fourth quarter, enabling customers to deploy Instinct faster and with higher performance across a broader range of workloads. Millions of large language and multimodal models run out of the box on AMD with the leading models launching with day zero support for Instinct GPUs. This capability highlights our rapidly expanding open source community enablement, including new upstream integration of AMD GPUs in DLLM, one of the most widely used inference engines.

To drive Instinct adoption with industry-specific use cases, we're also adding support for domain-specific models in key verticals. As one example, in health care, we added ROCm Support for the leading medical imaging framework to enable developers to train and deploy highly performing deep learning models on Instinct GPUs. For large businesses, we introduced our enterprise AI suite, a full stack software platform with enterprise-grade tools, inference microservices and solutions blueprints designed to simplify and accelerate production deployments at scale.

We also announced a strategic partnership with Tata Consultancy Services to co-develop industry-specific AI solutions and help customers deploy AI across their operations. Looking ahead, customer engagements for our next-gen MI400 series and Helios platform continue expanding. In addition to our multi-generation partnership with OpenAI to deploy 6 gigawatts of Instinct GPUs, we are in active discussions with other customers on at-scale multiyear deployments starting with Helios and MI450 later this year.

With the MI400 series, we are also expanding our portfolio to address the full range of cloud, HPC and enterprise AI workloads. This includes MI455X and Helios for AI superclusters, MI430X for HPC and sovereign AI and MI440X servers for enterprise customers requiring leadership training and inference performance in a compact eight-GPU solution that integrates easily into existing infrastructure.

Multiple OEMs publicly announced plans to launch Helios systems in 2026 with deep engineering engagements underway to support smooth production ramps. In December, HPE announced that they will offer Helios racks with purpose-built HPE Juniper Ethernet switches and optimized software for high-bandwidth scale-up networking. And in January, Lenovo announced plans to offer Helios racks. MI430X adoption also grew in the quarter with new exascale class supercomputers announced by GENCI in France and HLRS in Germany.

Looking further ahead, development of our next-generation MI500 series is well underway. MI500 is powered by our CDNA 6 architecture built on advanced 2-nanometer process technology and features high-speed HBM4E memory. We are on track to launch MI500 in 2027 and expect MI500 to deliver another major leap in AI performance to power the next wave of large-scale multimodal models.

In summary, our AI business is accelerating with the launch of MI400 series and Helios representing a major inflection point for the business as we deliver leadership performance and TCO at the chip, compute tray and rack level. Based on the strength of our EPYC and Instinct road maps, we are well positioned to grow Data Center segment revenue by more than 60% annually over the next 3 to 5 years and scale our AI business to tens of billions in annual revenue in 2027.

Turning to Client and Gaming. Segment revenue increased 37% year-over-year to $3.9 billion. In client, our PC processor business performed exceptionally well. Revenue increased 34% year-over-year to a record $3.1 billion, driven by increased demand for multiple generations of Ryzen desktop and mobile CPUs. Desktop CPU sales set a record for the fourth consecutive quarter. Ryzen CPUs topped the bestseller list at major global retailers and e-tailers throughout the holiday period with strong demand across all price points in every region, driving record desktop channel sell-out.

In mobile, strong demand for AMD-powered notebooks drove record Ryzen PC sell-through in the quarter. That momentum extended into commercial PCs, where Ryzen adoption accelerated as we established a new long-term growth engine for our client business. Sell-through of Ryzen CPUs for commercial notebooks and desktops grew by more than 40% year-over-year in the fourth quarter, and we closed large wins with major telecom, financial services, aerospace, automotive, energy and technology customers.

At CES, we expanded our Ryzen portfolio with CPUs that further extend our performance leadership. Our new Ryzen AI 400 mobile processors deliver significantly faster content creation and multitasking performance than the competition. Notebooks powered by Ryzen AI 400 are already available with the broadest lineup of AMD-based consumer and commercial AI PCs set to launch throughout the year. We also introduced our Ryzen AI Halo platform, the world's smallest AI development system, featuring our highest-end Ryzen AI MAX processor with 128 gigabytes of unified memory that can run models with up to 200 billion parameters locally.

In gaming, revenue increased 50% year-over-year to $843 million. Semi-custom sales increased year-over-year and declined sequentially as expected. For 2026, we expect semi-custom SoC annual revenue to decline by a significant double-digit percentage as we enter the seventh year of what has been a very strong console cycle.

From a product standpoint, Valve is on track to begin shipping, its AMD-powered steam machine early this year and development of Microsoft's next-gen Xbox featuring an AMD semi-custom SoC is progressing well to support a launch in 2027. Gaming GPU revenue also increased year-over-year with higher channel sell-out driven by demand throughout the holiday sales period for our latest generation Radeon RX 9000 series GPUs. We also launched FSR 4 Redstone in the quarter, our most advanced AI-powered upscaling technology, delivering higher image quality and smoother frame rates for gamers.

Turning to our Embedded segment. Revenue increased 3% year-over-year to $950 million, led by strength with test and measurement and aerospace customers and growing adoption of our Embedded x86 CPUs. Channel sell-through accelerated in the quarter as end customer demand improved across several end markets, led by test, measurement and emulation. Design win momentum remains one of the clearest indicators of long-term growth for our embedded business, and we delivered another record year. We closed $17 billion in design wins in 2025, up nearly 20% year-over-year as we've now won more than $50 billion of embedded designs since acquiring Xilinx.

We also strengthened our Embedded portfolio in the quarter. We began production of our Versal AI Edge Gen 2 SoCs for low-latency inference workloads and started shipping our highest-end Spartan UltraScale+ devices for cost-optimized application. We also launched new embedded CPUs, including our EPYC 2005 series for network security and industrial edge applications, Ryzen P100 series for in-vehicle infotainment and industrial systems and Ryzen X100 series for physical AI and autonomous platforms.

In summary, 2025 was an excellent year for AMD, marking the start of a new growth trajectory for the company. We are entering a multiyear demand super cycle for high performance and AI computing that is creating significant growth opportunities across each of our businesses. AMD is well positioned to capture that growth with highly differentiated products, a proven execution engine, deep customer partnerships and significant operational scale.

And as AI reshapes the compute landscape, we have the breadth of solutions and partnerships required for end-to-end leadership from Helios in the cloud for at-scale training and inference to an expanded instinct portfolio for sovereign, supercomputing and enterprise AI deployment. At the same time, demand for EPYC CPUs is surging as Agentic and emerging AI workloads require high-performance CPUs to power head nodes and run parallel tasks alongside GPUs. And at the edge and in PCs where AI adoption is just beginning, our industry-leading Ryzen and Embedded processors are powering real-time on-device AI. As a result, we expect significant top line and bottom line growth in 2026, led by increased adoption of EPYC and Instinct, continued client share gains and a return to growth in our Embedded segment.

Looking further ahead, we see a clear path to achieve the ambitious targets we laid out at our Financial Analyst Day last November, including growing revenue at greater than 35% CAGR over the next 3 to 5 years, significantly expanding operating margins and generating annual EPS of more than $20 in the strategic time frame, driven by growth in all of our segments and the rapid scaling of our Data Center AI business.

Now I'll turn the call over to Jean to provide additional color on our fourth quarter results and full year results. Jean?

Thank you, Lisa, and good afternoon, everyone. I'll start with a review of our financial results and then provide our current outlook for the first quarter of fiscal 2026.

AMD executed very well in 2025, delivering record revenue of $34.6 billion, up 34% year-over-year, driven by 32% growth in our Data Center segment and 51% growth in our Client and Gaming segment. Gross margin was 52%, and we delivered record earnings per share of $4.17, up 26% year-over-year while continuing to invest aggressively in AI and the data center to support our long-term growth.

For the fourth quarter of 2025, revenue was a record $10.3 billion, growing 34% year-over-year, driven by strong growth in the Data Center and Client and Gaming segment, including approximately $390 million in revenue from MI308 sales to China, which was not included in our fourth quarter guidance. Revenue was up 11% sequentially, primarily driven by continued strong growth in Data Center from both server and data center AI business as well as a return to year-over-year growth in the Embedded segment.

Gross margin was 57%, up 290 basis points year-over-year. We benefited from the release of $360 million in previously writing down MI308 inventory reserves. Excluding the inventory reserve release and MI308 revenue from China, gross margin would have been approximately 55%, up 80 basis points year-over-year, driven by favorable product mix.

Operating expenses were $3 billion, an increase of 42% year-over-year as we continue to invest in R&D go-to-market activities to support our AI road map and long-term growth opportunities as well as higher employee performance-based incentives. Operating income was a record $2.9 billion, representing a 28% operating margin. Tax, interest and other resulted in a net expense of approximately $335 million.

For the fourth quarter, diluted earnings per share was a record $1.53, an increase of 40% year-over-year, reflecting strong execution and operating leverage in our business model.

Now turning to our reportable segment. Starting with the Data Center segment. Revenue was a record $5.4 billion, up 39% year-over-year and 24% sequentially, driven by strong demand for EPYC processors and the continued ramp of MI350 products. Data Center segment operating income was $1.8 billion or 33% of revenue compared to $1.2 billion or 30% a year ago, reflecting higher revenue and inventory reserve release, partially offset by continued investment to support our AI hardware and software road maps.

Client Gaming segment revenue was $3.9 billion, up 37% year-over-year, driven primarily by strong demand for our leadership AMD Ryzen processors. On a sequential basis, revenue was down 3% due to lower semi customer revenue. The client business revenue was a record $3.1 billion, up 34% year-over-year and 13% sequentially, led by strong demand from both the channel and the PC OEMs and continued market share gains.

The gaming business revenue was $843 million, up 50% year-over-year, primarily driven by higher semi customer revenue and strong demand for AMD Radeon GPUs. Sequentially, gaming revenue was down 35% due to lower semi customer sales. Client and Gaming segment operating income was $725 million or 18% of revenue compared to $496 million or 17% a year ago.

Embedded segment revenue was $950 million, up 3% year-over-year and 11% sequentially as demand strengthened across several end markets. Embedded segment operating income was $357 million or 38% of revenue compared to $362 million or 39% a year ago.

Before I review the balance sheet and cash flow, as a reminder, we closed the sale of ZT Systems manufacturing business to Sanmina in late October. The fourth quarter financial results of the ZT manufacturing business are reported separately in our financial statement as discontinued operations and are excluded from our non-GAAP financials.

Turning to the balance sheet and cash flow. During the quarter, we generated a record $2.3 billion in cash from continuing operations and a record of $2.1 billion in free cash flow. Inventory increased sequentially by approximately $607 million to $7.9 billion to support strong data center demand. At the end of the quarter, cash, cash equivalents and short-term investments were $10.6 billion.

For the year, we repurchased 12.4 million shares and returned $1.3 billion to shareholders. We ended the year with $9.4 billion authorization remaining under our share repurchase program.

Now turning to our first quarter 2026 outlook. We expect revenue to be approximately $9.8 billion, plus or minus $300 million, including approximately $100 million of MI308 sales to China. At the midpoint of our guidance, revenue is expected to be up 32% year-over-year, driven by strong growth in our Data Center and Client and Gaming segments and modest growth in our Embedded segment.

Sequentially, we expect revenue to be down approximately 5%, driven by seasonal decline in our Client Gaming and Embedded segment, partially offset by growth in our Data Center segment. In addition, we expect fourth quarter non-GAAP gross margin to be approximately 55% non-GAAP operating expense to be approximately $3.05 billion. Non-GAAP other net income to be approximately $35 million, non-GAAP effective tax rate to be 13% and diluted share count is expected to be approximately 1.65 billion shares.

In closing, 2025 was an outstanding year for AMD, reflecting disciplined execution across the business to deliver strong revenue growth, increased profitability and cash generation while investing aggressively in AI and innovation to support our long-term growth strategy. Looking ahead, we are very well positioned for continued strong top line revenue growth and earnings expansion in 2026 with a focus on driving data center AI growth, operating leverage and delivering long-term value to shareholders.

With that, I'll turn it back to Matt for the Q&A session.

Yes. Thank you very much, Jean. Operator, please go ahead and open the Q&A session. Thank you.

[Operator Instructions] And the first question comes from the line of Aaron Rakers with Wells Fargo.

Lisa, at your Analyst Day back in November, you seem to kind of endorse the high $20 billion AI revenue expectation that was out there on the Street for 2027. I know today, you're reaffirming the path to strong double-digit growth. So I guess my question is, can you talk a little bit about what you've seen as far as customer engagements, how those might have expanded? I think you've alluded to in the past, multiple multi-gigawatt opportunities. Just any -- just double-click on what you've seen for the MI455 and Helios platform from a demands shaping perspective as we look into the back half of the year?

Yes. Sure, Aaron. Thanks for the question. So first of all, I think the MI450 Series development is going extremely well. So we're very happy with the progress that we have. We're right on track for a second half launch and beginning of production. And as it relates to sort of the shape of the ramp and the customer engagements, I would say the customer engagements continue to proceed very well. We have obviously a very strong relationship with OpenAI, and we're planning that ramp starting in the second half of the year going into 2027. That is on track.

We're also working closely with a number of other customers who are very interested in ramping MI450 quickly, just given the strength of the product, and we see that across both inference and training. And that is the opportunity that we see in front of us. So we feel very good about sort of the data center growth overall for us in 2026 and then certainly going into 2027, we've talked about tens of billions of dollars of data center AI revenue, and we feel very good about that.

The next question comes from the line of Tim Arcuri with UBS.

Jean, I'm wondering if you can maybe give us a little bit of detail under the hood for the March guidance. I know you basically told us that -- you told us about what Embedded is going to be up a bit year-over-year. Client sounds like it's down seasonally, which I take to be maybe down 10%. So can you give us a sense maybe of the other pieces?

And then also, can you give us a sense of how Data Center GPU is going to ramp through the year? I know it's a back half loaded year, but I think people are thinking at least somewhere in the $14 billion range this year. That's what investors are thinking. I don't -- I'm not asking you to endorse that. But if you can give us a little flavor for sort of how the ramp will look for the year, that would be great.

Tim, thanks for your question. We're guiding one quarter at a time, but I can give you some color about our Q1 guide. First is right, sequentially, we guided a decline around 5%, but Data Center is actually going to be up. And when you think about it, right, our CPU business seasonal actually in a regular seasonal pattern, it's going to be down high single digit. And in our current guide, we actually guide CPU revenue up sequentially very nicely.

Also with the Data Center GPU side, we also feel really good about GPU revenue, including China, will be also up. So very nice guide for the Data Center overall. On the Client side, we do see seasonality sequentially decline. Embedded and Gaming, they also have a seasonal decline.

And maybe, Tim, if I just give you a little bit on the full year commentary. I think the important thing, as we look at the full year, we're very bullish on the year. We're not -- if you look at the key themes, we're seeing very strong growth in the Data Center, and that's across 2 growth vectors. We see server CPU growth actually very strong. I mean we've talked about the fact that CPUs are very important as AI continues to ramp. And we've seen the CPU order book continue to strengthen as we go through the last few quarters and especially over the last 60 days.

So we see that as a strong growth driver for us. As Jean said, we see server CPU growing from Q4 into Q1 in what normally is seasonally down, and that continues throughout the year. And then on the Data Center AI side, it's a very important year for us. It's really an inflection point. MI355 has done well, and we were pleased with the performance in Q4, and we continue to ramp that in the first half of the year. But as we get into the second half of the year, the MI450 is really an inflection point for us. So that revenue will start in the third quarter, but it will ramp significant volume in the fourth quarter as we get into 2027. So that gives you a little bit of sort of what the data center ramp looks like throughout the year.

And the next question comes from the line of Vivek Arya with Bank of America.

First, just a clarification on what you're assuming for your China MI308 sales beyond Q1. And then Lisa, specific to 2026, can your Data Center revenues grow at your target 60% plus growth rate? I realize that, that's a multiyear target, but do you think that there are enough drivers, whether it's on the server CPU side or GPU side for you to grow at that target base even in 2026?

Yes. Sure, Vivek. So let me talk a little bit about China first because that's, I think, important for us to make sure that's clear. Look, we were pleased to have some MI308 sales in the fourth quarter. They were actually a license that was approved through work with the administration. And those orders were actually from very early in 2025. And so we saw some revenue in Q4, and we're forecasting for about $100 million of revenue in Q1. We are not forecasting any additional revenue from China just because it's a very dynamic situation.

So given that it's a dynamic situation, we're still waiting for -- we've submitted licenses for the MI325, and we're continuing to work with customers and understanding sort of their customer demand. We thought it prudent not to forecast any additional revenue other than the $100 million that we called out in the Q1 guide.

Now as it relates to overall Data Center, as I mentioned in the question to Tim, like we're very bullish about data center. I think the combination of drivers that we have across our CPU franchise, I mean, the EPYC product line, both Turin and Genoa continue to ramp well. And in the second half of the year, we will be launching Venice, which we believe actually extends our leadership and the MI450 ramp, which is also very significant in the second half of 2026. We're not obviously guiding specifically by segment, but the long-term target of, let's call it, greater than 60% is certainly possible in 2026.

[Operator Instructions] The next question comes from the line of C.J. Muse with Cantor.

I'm curious on the server CPU side of the house and given the dramatic tightness, curious your ability to source incremental capacity from TSMC and elsewhere. And I guess, how long will it take for that to see wafers out? And how should we think about the implications for kind of the growth trajectory throughout all of calendar '26? And I guess as part of that, if you could speak to how we should be thinking about inflection in pricing as well, that would be very helpful.

Sure, C.J. So a couple of points about the server CPU market. First of all, we think the overall server CPU TAM is going to grow, let's call it, strong double digits in 2026, just given the -- as we said, the relationship between CPU demand and overall AI ramp. So I think that's a positive.

Relative to our ability to support that, we've been seeing the trend for the last couple of quarters. So we have increased our supply capacity capability for server CPUs. And that's one of the reasons we're able to increase our Q1 guide as it relates to the server business, and we see the ability to continue to grow that throughout the year. There's no question that demand continues to be strong. And so we're working with our supply chain partners to increase supply as well. But from what we see today, I think the overall server situation is strong, and we are increasing supply to address that.

C.J., do you have a follow-up question?

I do maybe for Jean, if you could kind of touch on gross margins through the year and as you balance kind of strengthening server CPU with perhaps greater GPU accelerating in the second half. Is there kind of a framework that we should be working off of?

Yes. Thank you for the question. We are very pleased with our gross margin Q4 performance and the Q1 guide at 55%, which actually is 130 basis points up year-over-year, while we continue to ramp our MI355 year-over-year very significantly. I think we are benefiting from a very favorable product mix across all our business. If you think about in Data Center, we're ramping our new product, new generation product, Turin and MI355, which helps the gross margin in client. We continue to move up the stack and also gaining momentum in our commercial business.

Our client business gross margin has been improving nicely. In addition, certainly, we see the recovery of our Embedded business, which is also margin accretive. So all those tailwinds we are seeing, we continue to see in the next few quarters. And when MI450 ramp, of course, in Q4, our gross margin will be driven largely by mix. And I think we'll give you more color when we get there. But overall, we feel really good about our gross margin progression this year.

The next question comes from the line of Joe Moore with Morgan Stanley.

On the MI455 ramp, will 100% of the business be racks? Will there be kind of an 8-way server business around that architecture? And then is the revenue recognition when you ship to the rack vendor? Or is there something to understand about that?

Yes, Joe. So we do have multiple variants of the MI450 series, including an 8-way GPU form factor. But for 2026, I would say the vast majority of it is going to be Rack Scale solutions. And yes, we will take revenue when we ship to the rack builder.

Okay. Great. And then can you talk to any risks that you may have in terms of once you get silicon out, turning that into racks, any potential issues as you ramp that? I know your competitor had some last year, and you said you learned from that. Is there anything you've done with kind of prebuilding racks to sort of ensure you won't have those issues? Just any risk that we need to understand around that?

Yes. I mean, I think, Joe, the main thing is the development is going really well. It is -- we're right on track with the MI450 series as well as the Helios rack development. We've done a lot of testing already both at the Rack Scale level as well as at the silicon level. So far, so good. We are getting, let's call it, a lot of input from our customers on things to test so that we can do a lot of testing in parallel. And our expectation is that we will be on track for our second half launch.

Our next question comes from the line of Stacy Rasgon with Research.

First, Lisa, I just wanted to ask about OpEx. Like every quarter, you guys are guiding it up and then it's coming in even higher and then you're guiding it up again. And I understand, given the growth trajectory that you need to invest. But how should we think about the ramp of that OpEx and that spending number, especially as the GPU revenue starts to inflect? Do we get leverage on that? Or should we be expecting the OpEx to be growing even more materially as the AI revenue starts to ramp?

Yes. Sure, Stacy. Thanks for the question. Look, I think in terms of OpEx, we're at a point where we have very high conviction in the road map that we have. And so in 2025, as the revenue increased. We did lean in on OpEx, and I think it was for all the right reasons. As we get into 2026 and as we see some of the significant growth that we're expecting, we should absolutely see leverage. And the way to think about it is we've always said in our long-term model that OpEx should grow slower than revenue, and we would expect that in 2026 as well, especially as we get into the second half of the year and we see inflection in the revenue. But at this point, I think the -- if you look at our free cash flow generation and the overall revenue growth, I think the investment in OpEx is absolutely the right thing to do.

For my follow-up, I actually have 2 sort of one line answers I'm looking for. Just first, the $100 million in China revenue in Q1, does that also drop through a 0 cost basis like we had in Q4? And is that a margin headwind? And number two, I know you don't give us the AI number, but could you just give us the annual like 2025 Instinct number now that we're through the year? Like how big was it?

So Stacy, let me answer your first question on the $100 million revenue in Q1. Actually, the inventory reserve reversed in Q4, which was $360 million, not only associated with the Q4 revenue, China revenue, but also covers the $100 million revenue we expect to ship in Q1 to China with our MI308. So the Q1 gross margin guide is a very clean guide.

And Stacy, for your second question, as you know, we don't guide at the business level, but to help you with your models, I think you can -- if you look at the Q4 data center AI number, even if you were to back out the China number, which was, let's call it, not a recurring number, you would still see growth -- you'll see growth from Q3 to Q4. So that should help you a little bit with your modeling.

And the next question comes from the line of Joshua Buchalter with TD Cowen.

I want to ask about clients. So the segment beat pretty handily in the fourth quarter. and I recognize you guys have been gaining share with Ryzen. But I think given what we've been seeing in the memory market, there's a lot of concern about inflationary costs and the potential for pull-ins. Were there any changes in your order patterns during the quarter? And maybe bigger picture, how are you thinking about client growth and the health of that market into 2026?

Yes. Thanks for the question, Josh. The client market has performed extremely well for us throughout 2025, very strong growth for us, both in terms of ASP mixing up the stack as well as just unit growth. Going into 2026, we are certainly watching the development of the business. I think the PC market is an important market. Based on everything that we see today, we're probably seeing the PC TAM down a bit just given some of the inflationary pressures of the commodities pricing, including memory.

The way we are modeling the year is, let's call it, second half a bit subseasonal to first half, just given everything that we see. Even in that environment with the PC market down, we believe we can grow the -- our PC business. And -- our focus areas are enterprise. That's a place where we're making a very nice progress in 2025, and we expect that into 2026 and just continuing to grow sort of at the premium higher end of the market.

And then I want to ask about the Instinct family. So we've seen your big GPU competitor make a deal with an SRAM-based spatial architecture provider and then OpenAI has reportedly been linked to one as well. Could you speak to the competitive implications of that? You've done well in inferencing, I think, partly because of your leadership in HBM content. So I was wondering if you could maybe address the pull seemingly motivated by lower latency inference and how Instinct is positioned to service this if you're indeed seeing it as well.

Yes. I think, Josh, it's really, I think, the evolution that you might expect as the AI market matures. What we're seeing is as inference ramps, the -- really the tokens per dollar or the efficiency of the inference stack becomes more and more important. As you know, with our chiplet architecture, we have a lot of ability to optimize across inference training and even across sort of the different stages of inference as well.

So I think I view this as very much as you go into the future, you'll see more workload optimized products. And you can do that with GPUs as well as with other more ASIC-like architectures. I think we have the full compute stack to do all of those things. And from that standpoint, we're going to continue to lean into inference as we view that as a significant opportunity for us in addition to ramping our training capabilities.

And the next question comes from the line of Ben Reitzes with Melius Research.

Lisa, I wanted to ask you about OpenAI. I'm sure a lot of the volatility out there is not lost on you. Is everything on track for the second half for starting the 6 gigawatts and the 3.5-year time line as far as you know? And is there any other color that you'd just like to give on that relationship? And then I have a follow-up.

Yes. I mean I think, Ben, what I would say is we're very much working in partnership with OpenAI as well as our CSP partners to deliver on MI450 series and deliver on the ramp. The ramp is on schedule to start in the second half of the year. MI450 is doing great. Helios is doing well. We are in, let's call it, deep co-development across all of those parties. And as we look forward, I think we are optimistic about the MI450 ramp for OpenAI. But I also want to remind everyone that we have a broad set of customers that are very excited about MI450 series. And so in addition to the work that we're doing with OpenAI, there are a number of customers that we're working to ramp in that time frame as well.

All right. I appreciate that. And I wanted to shift to the server CPU and just talk about x86 versus ARM. There's some view out there that x86 has a particular edge in agents, big picture, do you agree with that? And what are you seeing from customers? And in particular, obviously, your big competitor is going to be selling an ARM CPU separately now in the second half. So if there's just anything on that competitive dynamic versus ARM and what NVIDIA is doing and your views on that, that would be great to hear.

Yes, Ben, what I would say about the CPU market is there is a great need for high-performance CPUs right now. And that goes towards Agentic workloads where when you have these AI processes or AI agents that are spinning off a lot of work, in an enterprise, they're actually going to a lot of traditional CPU tasks and the vast majority of them are on x86 today. I think the beauty of EPYC is that we've optimized. We've done workload optimization. So we have the best cloud processor out there. We have the best enterprise processor. We also have some lower-cost variants for storage and other elements. And I think all of that comes into play as we think about the entirety of the AI infrastructure that needs to be put in place.

I think the CPUs are going to continue to be as important as a piece of the AI infrastructure ramp. And that's one of the things that we mentioned at our Analyst Day back in November, is really this multiyear CPU cycle, and we continue to see that. I think we've optimized EPYC to satisfy all of those workloads, and we're going to continue to work with our customers to expand our EPYC footprint.

And the next question comes from the line of Tom O'Malley with Barclays.

Lisa, I just wanted to ask, you mentioned on memory earlier as a sticking point in terms of inflationary cost. Different customers do this in different ways, different suppliers do this in different ways. But can you maybe talk about your procurement of memory, when that takes place, particularly on the HBM side? Is that something that gets done a year in advance, 6 months in advance? Different accelerator guys have talked about different time lines. I would be curious to kind of hear when you do the procurement.

Yes. I mean, given the lead times for things like HBM and wafers and these parts of the supply chain, I mean, we're working closely with our suppliers over a multiyear time frame in terms of what we see in demand, how we ramp, how we ensure that our development is very closely tied together. So I feel very good about our supply chain capabilities. We have been planning for this ramp. So independent of the current market conditions, we've been planning for a significant ramp in our -- both CPU as well as our GPU business over the past couple of years. And so from that standpoint, I think we're well positioned to grow substantially in 2026. And now we are also doing multiyear agreements that extend beyond that given the tightness of the supply chain.

And just as a follow-up, you've seen a variety of different things in the industry in terms of system accelerators, so cache offload, more discrete ASIC style compute, CPX. If you look at what your competitors are doing and you look at your first generation of system architecture coming out, maybe spend some time on -- do you see yourself following in the footsteps of some of these different type of architectural changes? Do you think that you'll go in a different direction? Anything just on the evolution of your system-based architecture and then the adjoining products and/or silicon within?

I think, Tom, what we have is the ability with a very flexible architecture, with our chiplet architecture, and then we also have a flexible platform architecture that allows us to really have different system solutions for the different requirements. I think we're very cognizant that there will be different solutions. So there's no -- I've often said there's no one size fits all, and I'll say that again, there's no one size fits all.

But that being the case, it's clear that the Rack Scale architecture is very, very good for the highest end applications when you're talking about inference -- distributed inference and training. But we also see an opportunity with enterprise AI to use some of these other form factors. And so we're investing across that spectrum.

And the next question comes from the line of Ross Seymore with Deutsche Bank.

A couple of I guess my first question is back on the gross margin side of things. As you go from the MI300 to the 400 to the 500 eventually, do you see any changes in the gross margin throughout that period? In the past, you've talked about optimizing dollars more so than percentages. But just on the percentage side, does it go up, down? Or is there volatility as you go from one to the next for any reason? Just wondered on the trajectory there.

Ross, thank you for the question. At a very high level, each generation, we actually provide much more capabilities, more memory, help our customers more. So in general, the gross margin should progress each generation when you offer more capabilities to your customers. But typically, when you first ramp at the beginning of ramp of generation, it tends to be lower when you get to the scale, get to the yield improvement, the testing improvement and also overall performance improvement, you will see gross margin improving within each generation. So it's kind of a dynamic gross margin. But in the longer term, you should expect each generation should have a higher gross margin.

And then one on a small segment of your business, but it seems quite volatile and you talked a little bit about further off than you usually do is the gaming side of things. What is the magnitude down you're talking about this year? Because in 2025, you thought it was going to be flat and it ended up growing 50%, which was a nice positive surprise. But now that you're talking about this year being down, but then the next-gen Xbox ramping in 2027, I just hope to get some color on what you see as kind of the annual trajectory there.

Yes. So Lisa can add more. So 2026, actually, it's the seventh year of current product cycle. Typically, when you're at this stage of the cycle, revenue tend to come down. We do expect the revenue on the semi customer revenue side to come down significantly double digit for 2026, as Lisa mentioned in her prepared remarks, the next generation?

Yes. I think we'll -- I mean we'll certainly talk about that going forward. But as we ramp the new generation, you would expect a reversal of that.

Operator, I think we have time for one more caller on the call, please.

And our final question comes from the line of Jim Schneider with Goldman Sachs.

Relative to the ramp of your recallable systems, would you expect any kind of bottleneck in terms of supply constraints in terms of the ramp as you ramp the second half of the year to potentially impact or limit the revenue growth? In other words, maybe talk about whether you expect supply to really kind of mute the growth in Q4 sequentially relative to -- sorry, Q3 relative to Q4?

Yes. Jim, we are planning this at the -- every component level. So I think relative to our data center AI ramp, I do not believe that we will be supply limited in terms of the ramp that we put in place. I think we have an aggressive ramp. I think it's a very doable ramp. And as we think about the size and scale of AMD, clearly, our priority is ensuring that the data center ramps go very well, and that's both on the data center AI, the GPU side as well as on the CPU side.

And then maybe as a follow-up to the earlier question on OpEx. Could you maybe address what are some of the largest investment areas you made in 2025? And then what are the largest incremental OpEx investment areas for '26?

Yes, Jim, on the 2025 investment, the priority and the investment -- largest investment in Data Center AI. Our hardware road map, we accelerated that road map. We expand our software capabilities. We also acquired ZT Systems, which added significant system-level solutions and capabilities. Those are the primary investment in 2025.

We also invest heavily in go-to-market to really expand our go-to-market capabilities to support the revenue growth and also expand our commercial business and enterprise business for our CPO franchise. In 2026, you should expect us to continue to invest aggressively. But as Lisa mentioned earlier, we do expect revenue to expand faster than operating expense increase to drive the earnings per share expansion.

All right. Thank you, everybody, for participating on the call. Operator, I think we can go ahead and close the call now. Thank you. Good evening.

Thank you. And ladies and gentlemen, that does conclude the question-and-answer session, and that also concludes today's teleconference. You may disconnect your lines at this time, and have a great rest of the day.

So ladies and gentlemen, it is my privilege to welcome to the stage a globally respected technologist, an industry-defining CEO and a leader whose work continues to shape the very trajectory of modern computing. Ladies and gentlemen, please join me in welcoming to the stage, Chair and CEO of AMD, Dr. Lisa Su.

All right. What an audience. How are you guys doing tonight? That sounds wonderful.

First of all, thank you, Gary, and welcome to everyone here in Las Vegas and joining us online. It's great to be here with all of you to kick off CES 2026. And I have to say, every year, I love coming to CES to see all the latest and greatest tech and catch up with so many friends and partners. But this year, I'm especially honored to be here with all of you to open CES.

Now we have a completely packed show for you tonight, and it will come as no surprise that tonight is all about AI. Although the rate and pace of AI innovation has been incredible over the last few years, my theme for tonight is "you ain't see nothing yet". We are just starting to realize the power of AI. And tonight, I'm going to show you a number of examples of where we're headed, and I'll be joined by some of the leading experts in the world from industry giants to breakthrough start-ups, and together, we are working to bring AI everywhere and for everyone. So let's get started.

At AMD, our mission is to push the boundaries of high performance and AI computing to help solve the world's most important challenges. Today, I'm incredibly proud to say that AMD technology touches the lives of billions of people every day. From the largest cloud data centers to the world's fastest supercomputers to 5G networks, transportation and gaming, every one of these areas is being transformed by AI. AI is the most important technology of the last 50 years, and I can say it's absolutely our #1 priority at AMD. It's already touching every major industry, whether you're going to talk about healthcare or science or manufacturing or commerce, and we're just scratching the surface. AI is going to be everywhere over the next few years. And most importantly, AI is for everyone. It makes us smarter. It makes us more capable. It enables each one of us to be a more productive version of ourselves. And at AMD, we're building the compute foundation to make that future real for every company and for every person.

Now since the launch of ChatGPT a few years ago, I'm sure we all remember the first time we tried it, we've gone from 1 million people using AI to now more than 1 billion active users. This is just an incredible ramp. It looked -- it took the Internet decades to reach that same milestone. Now what we are projecting is even more amazing. We see the adoption of AI growing to over 5 billion active users as AI truly becomes indispensable to every part of our lives, just like the cell phone and the Internet of today. Now the foundation of AI is compute. With all of that user growth, we have seen a huge surge in demand in the global compute infrastructure, growing from about 1 zetaflop in 2022 to more than 100 zetaflop in 2025. Now that sounds big. That's actually 100 times in just a few years. But what you're going to hear tonight from everyone is we won't have -- we don't have nearly enough compute for everything that we can possibly do. We have incredible innovation happening. Models are becoming much more capable. They're thinking and reasoning, they're making better decisions, and that goes even further when we extend that to agents overall.

So to enable AI everywhere, we need to increase the world's compute capacity another 100x over the next few years to more than 10 yottaflop over the next 5 years. Now let me take a survey. How many of you know what a Yottaflop is? Raise your hand, please. A Yottaflop is a followed by 24 zeros. So 10 Yottaflop is 10,000x more compute than we had in 2022. There's just never, ever been anything like this in the history of computing. And that's really because there's never been a technology like AI.

Now to enable this, you need AI in every compute platform. So what we're going to talk about tonight is the whole gamut. We're going to talk about the cloud where it runs continuously delivering intelligence globally. We're going to talk about PCs where it helps us work smarter and personalize every experience that we have. And we're going to talk about the edge where it powers machines that can make real-time decisions in the real world. AMD is the only company that has the full range of compute engines to make this vision a reality. You really need to have the right compute for each workload. And that means GPUs, that means CPUs, that means NPUs, that means custom accelerators. We have them all. And each of them can be tuned for the application to give you the best performance as well as the most cost-effective solution.

So tonight, we're going to go on a journey. So you're going to go with me through several chapters as we showcase the latest AI innovations across cloud, PCs, health care and much more. So let's go ahead and start with the first chapter, which is the Cloud. The cloud is really where the largest models are trained and where intelligence is delivered to billions of users in real time. For developers, the cloud gives them instant access to massive compute, the latest tools and the ability to deploy and scale as use cases take off. The cloud is also where most of us experience AI today. So whether you're using ChatGPT or Gemini or Grok or you're coding with Copilots, all of these powerful models are running in the cloud.

Now today, AMD is powering AI at every level of the cloud. Every major cloud provider runs on AMD EPYC CPUs. And 8 of the top 10 AI companies use Instinct accelerators to power their most advanced models. And the demand for more compute is just continuing to go up. Let me just show you a few graphs. Over the past decade, the compute needed to train the leading AI models has increased more than 4x every year, and that trend is just continuing. That's how we're getting today's models that are dramatically smarter and more useful. At the same time, as more people are using AI, we've seen an explosion over the last 2 years of inference, growing the number of tokens 100x, really hitting an inflection point. You can just see how much that inference is really taking off. And to keep up with this compute demand, you really need the entire ecosystem to come together. So what we like to say is the real challenge is how do we put AI infrastructure at [indiscernible] scale, and that requires more than just raw performance. It starts with leadership compute, CPUs, GPUs, networking coming together. It takes an open modular rack design that can evolve over product generations. It requires high-speed networking to connect thousands of accelerators into a single unified system. And it has to be really easy to deploy. So we want full turnkey solutions.

That's exactly why we built Helios, our next-generation rack scale platform for the yota scale AI era. Helios requires innovation at every single level, hardware, software and systems. It starts with our engineering teams who designed our next-generation Instinct MI455 Accelerators to deliver the largest generational performance increase we've ever achieved. MI455 GPUs are built using leading-edge 2-nanometer and 3-nanometer process technologies and advanced 3D chiplet packaging with ultrafast high-bandwidth HBM4 memory. This is integrated into a compute tray with our EPYC CPUs and Pensando networking chips to create a tightly integrated platform. Each tray is then connected with high-speed ultra accelerator link protocol tunneled over ethernet, which enables the 72 GPUs in the rack to function as a single compute unit.

And then from there, we can connect thousands of Helios racks to build powerful AI clusters using industry standard Ultra Ethernet NICs and Pensando-programmable GPUs that can accelerate AI performance even more by offloading some of the tasks from the GPUs. Now we are at CES. It is a little bit about show and tell. So I am proud to show you Helios right here in Vegas, the world's best AI rack.

[Video Presentation]

Now for those of you who have not seen a rack before, let me tell you Helios is a monster of a rack. This is no regular rack, okay? This is a double-wide design based on the OCP Open Rack Wide standard developed in collaboration with Meta, and it weighs nearly 7,000 pounds. So Gary, it took us a bit to get it up here, just so you know. But we wanted to show you what is really powering all this AI. It is actually more than 2 compact cars. Now the way we've designed Helios was really working closely with our lead customers, and we chose this design so that we could optimize serviceability, manufacturability and reliability for next-generation AI data centers. Now let me show you a few other things.

At the center of Helios is the compute tray. So let's take a closer look at what one of those trays look like. Now I can tell you, I probably cannot lift this compute tray, so it had to come out. But let me just describe it a little bit. Each Helios compute tray includes 4 MI455 GPUs, and they're paired with the next-gen EPYC Venice CPU and Pensando networking chips. And all of this is liquid cooled so that we can maximize performance. At the heart of Helios is our next-generation Instinct GPUs. And you guys have seen me hold up a lot of chips in my career. But today, I can tell you, I am genuinely excited to hold up this chip. So let me show you mi455X for the very first time. 455 is the most advanced chip we've ever built. It's pretty darn big. It has 320 billion transistors, 70% more than mi355. It includes 12 2-nanometer and 3-nanometer compute and I/O chiplets and 432 gigabytes of ultrafast HBM4, all connected with our next-gen 3D chip stacking technology. So we put 4 of these into the compute trays up here.

And then driving those GPUs is our next-generation EPYC CPU code named Venice. Venice extends our leadership across every dimension that matters in the data center, more performance, better efficiency and lower total cost of ownership. Now let me show you Venice for the first time. I have to say this is another beautiful chip. I do love our chips, so I can say that for sure. Venice is built with 2-nanometer process technology and features up to 256 of our newest high-performance Zen 6 cores. And the key here is we actually designed Venice to be the best AI CPU. We doubled the memory and GPU bandwidth from our prior generation. So Venice can feed mi455 with data at full speed even at rack scale. So this is really about co-engineering. And we tie it all together with our 800-gig Ethernet Pensando Vulcano and Salina networking chips, delivering ultra-high bandwidth as well as ultra-low latency, so tens of thousands of Helios racks can scale across the data center. Now just to give you a little bit of the scale of what this means, that means that each Helios rack has more than 18,000 CDNA 5 GPU compute units and more than 4,600 Zen 6 CPU cores, delivering up to 2.9 exaflops of performance.

Each rack also includes 31 terabytes of HBM4 memory, an industry-leading 260 terabytes per second of scale-up bandwidth and 43 terabytes per second of aggregate scale-out bandwidth to move data in and out incredibly fast. Suffice it to say, those numbers are big. When we launched Helios later this year, and I'm happy to say Helios is exactly on track to launch later this year, we expect it will set the new benchmark for AI performance. And just to put this performance in context, just over 6 months ago, we launched mi355, and we delivered up to 3x more inference throughput versus the prior generation. And now with mi455, we're bending that curve further, delivering up to 10x more performance across a wide range of models and workloads. That is game changing.

mi5455 allows developers to build larger models, more capable agents and more powerful applications. And no one is pushing faster and further in each one of these areas than OpenAI. To talk about where AI is headed and the work that we're doing together, I'm extremely happy to welcome the President and Co-Founder of OpenAI, Greg Brockman to the stage.

Greg, it is so great to have you here. Thank you for being here. OpenAI truly started all of this with the release of ChatGPT a few years ago, and the progress you've made is just incredible. We're absolutely thrilled about our deep partnership. Can you just give us a picture of where are things today? What are you seeing? And how are we working together?

Well, first of all, it's great to be here. Thank you for having me. ChatGPT is very much the overnight success that was 7 years in the making, right? -- that we started OpenAI back in 2015 with a vision that deep learning could lead to artificial general intelligence to very powerful systems that could benefit everyone. And we wanted to help to actually realize that technology and bring it to the world and democratize it. And we spent a long time just making progress where year-over-year, the benchmarks look better and better. But the first time we had something that was so useful that many people around the world wanted to use it was ChatGPT. And we were just blown away by the creativity and the ways in which people found how to really leverage the models we have produced in their daily lives. And so just out of curiosity, how many people in the room are ChatGPT users?

That -- pretty much like the whole room.

I'm glad to hear it. But very importantly, how many of you have had an experience that was very key to your life or the life of a loved one, whether it's in health care, in helping manage a newborn and any other walk of your life?

And to me, that's the metric that we want to optimize and that seeing that number go up and to the right has been something that has been really different over 2025, right, that we really moved from being just a text box that you ask a question, you get an answer, something very simple contained to people really using it for very personal, very important things in their lives. And it's not just in personal lives, health care and aspects like that. It's also in the enterprise, right, and really starting to bring models like [indiscernible] to be able to transform software engineering. And I think that this year, we're really going to see enterprise agents really take off. We're seeing scientific discovery start to be really accelerated, whether it's developing novel math proofs. The first time we saw that was just a couple of months ago, and the progress is continuing. And it's really across every single endeavor of human knowledge work, where there's human intelligence that can be leveraged, right? You can amplify it. We now have an assistant, right? We now have the tool, we have an adviser that is able to amplify what people want to do.

I completely agree with you, Greg. I think we have seen just an enormous acceleration of what we're using this tech for. Now I would say, I think every single time I see you, you tell me you need more compute.

It's true. It's true.

It's almost like a broken record, you could just just Greg wants more compute. Can you talk about just some of the things that you're seeing in the infrastructure, some of the bottlenecks? And where do you think we should be focusing as an industry?

Well, the why, why do we need more compute is the most important question, right, which is really when the models are not that capable, right, and where we were in 2015, 2016, 2017 and so forth is that you basically just want to train a model and evaluate it, right? Maybe there would be a very narrow task to be useful for. But as we've made this exponential progress on the models, then there's actually exponential utility to them. People want to bring it into their lives in a very scalable way. And I think that what we're seeing is as we move from -- you ask a question, you get an answer to agentic workflows where you ask the model to write some software for you, and it goes off for minutes or hours or so even days. And you're not just operating one agent, you're operating a fleet of agents. right?

You can have 10 different work streams all going at once on your behalf for a single developer, right? And it should be the case that you wake up in the morning, and this is the kind of thing we are going to build, by the way, and ChatGPT has taken items off your to-do list at home and at work. And that all of that, that's going to require that big graph that you had of how much compute the world is going to need. That's going to require far more compute than we have right now. Like I would love to have a GPU running in the background for every single person in the world because I think it can deliver value for them, but that's billions of GPUs. No one has a plan to build that kind of scale. And so what we're really seeing is benefits in people's lives, right?

We're seeing, for example, some of my favorite applications and some of the ones I think are the most important are in healthcare, right, that we actually see people's lives being saved through ChatGPT. Just over the holidays, one of my coworkers says that her husband had leg pain. They went to the hospital, they went to the ER and they got it X-ray and the doctors are like, "Oh, it's a cold muscle, just wait it out, you'll be fine. They went home, it got a little bit worse, type symptoms into ChatGPT. ChatGPT said, go back to the ER, this could be a blood clot. And in fact, it was. It's deep [indiscernible] thrombrosis in the leg and in addition to 2 blood clots on the lungs. And if they just waited it out, that would have been likely fatal. And it's not a unique story.

CG, our CEO of Applications, I work very closely with every day, ChatGPT literally saved her life, too, right? She was in the hospital for a kidney stone and had an infection. They're about to inject an antibiotic. And she said, wait just a moment, she asked ChatGPT whether that one is safe for her. ChatGPT, which has all of her medical history, said, no, no, because you had this other infection 2 years ago, that could re-trigger it, and that could actually be life-threatening as well. And so she showed it to the doctor. The doctor is like, wait, what you had this condition. I didn't know. I only had 5 minutes to review your medical history.

I completely agree, Greg. I mean that's one of the things that we can always -- all of us can use a helper, and that's really what we have here. Look, I mean, I think you've painted a vivid picture of why we need more compute, of what we can do with AI. I think we feel exactly the same way.

Now we've also done an incredible amount of work with your engineering teams. mi455 and Helios is actually -- a lot of it is through some of the feedback from our engineering teams working closely together. Can you talk a little bit about that infrastructure? And what are your customers wanting? And how are you going to use mi455?

Well, one of the key things with how AI is evolving is thinking about the balance of different resources on the GPU. And so we have a slide to show how we've seen the evolution of this -- the balance of resources across different mi generations. So you see the slide that I very painstakingly put together. Actually, I did not painstakingly put it together. I asked ChatGPT to put it -- to go create the slide. And so it literally did all the research, and you can see some sources at the bottom and it actually went and read a bunch of different AMD materials, created these charts, put together, the title put together all of these headers and produce not just an answer for me to then go do a bunch of work and produce an artifact, an artifact that I can show. And this is just one simple example that you can do today with ChatGPT, right, that we are moving to a world where you are going to be able to have an agent that does all this work for you.

And for that, we're going to need to have hardware that is really tuned to our applications. What we have in mind is that we're moving to a world where human attention, human intent becomes the most precious resource. And so that there should be very low latency interaction anytime that human involved, or there should be an ocean of agentic compute that's constantly running. It's very high throughput. And these two different regimes of low-latency and high throughput yield a bunch of different pressures on hardware manufacturers such as yourself. So it's a pleasure to be working together.

We like building GPUs for you. That works well. Look, lastly, Greg, let's talk a little bit about the future. Paint the picture, one of the things that we've talked about is there are some people out there who are wondering, is the demand really there? Can AI compute like do we really need all of this AI compute? And I know you and I have talked about it. I think people don't have a view of the future that you see. I mean you have like a special seat. So paint the world for what this looks like in a few years?

Well, looking backwards, we have been tripling our compute every single year for the past couple of years, and we've also tripled our revenue. And the thing that we find within OpenAI is every time we want to release a new feature, we want to produce a new model, we want to bring this technology to the world. We have a big fight internally over compute, because there are so many things we want to launch and produce for all of you that we simply cannot because we are compute constrained.

And I think we're moving to a world where GDP growth will itself be driven by the amount of compute that is available in a particular country, in a particular region. And I think that we're starting to see the first [indiscernible] of this. And I think over the next couple of years, we'll see it start to hit in a real way. And I think that AI is something where I think data centers can actually be very beneficial to local communities. I think that's a really important thing for us to really prove to people. But also the AI technology we produce, that is also something where in terms of scientific advances, you think about what has been the most fundamental driver of increase in quality of life, right? It really is about science.

And every time we've gone into specific domains, you just see how much limitation there is from how things are done because that's just -- there's a particular discipline that's built a bunch of expertise. There are a small number of experts and then it's hard for them to propagate that to future generations. For example, in biology, we hooked up GP5 to a [indiscernible] setup and had humans described what the [indiscernible] looked like. The model said here, a couple of ideas to try, the humans will go try it, and it actually produced a 79x, almost 100x fold improvement in the efficiency of a particular protocol. And that's just one particular reaction that people have spent some time actually optimizing, but not like a ton and ton of time because it's just there's so much surface area available in biology that no human can possibly get to all of it. No human can be an expert across every single subfield.

And I think what we're going to see is AIs that really bridge across disciplines that humanity has been unable to bridge, right? You see this within health care, where as humans learn more, we specialize more, but we're going to have AI that's going to amplify. And so I think it will be for hard problems that AI will be brought to bear. This will be true for enterprise. Every single application, I think we'll have an agent that is accelerating what people want to do. And I think the hardest problem for humanity will be deciding how do we use the limited resources we have to get the most benefits for everyone.

That is an incredible vision. Greg, we are so excited to be working with you. I think there's no question in the world that we have the power to really change people's lives. Thank you for the partnership and really look forward to it.

Thank you.

So as you heard from Greg, compute is key, and mi455 is a game changer. But with the mi400 series, we designed a full portfolio of solutions for cloud, enterprise, supercomputing and sovereign AI. At the top is Helios that's built for the leading-edge performance, hyperscale training and distributed inference at rack scale. For enterprise AI deployments, we have instinct mi440X GPUs that deliver leadership training and inference performance in a compact 8-GPU server designed for easy use in today's existing data center infrastructure. And for sovereign AI and supercomputing, where extreme accuracy matters the most, we have the mi430X platform that delivers leadership hybrid computing capabilities for both high-precision scientific and AI data types. This is something unique that we at AMD do because of our chiplet technology, we can actually have the right compute for the right application.

Now hardware is only part of the story. We believe an open ecosystem is essential to the future of AI. Time and time again, we've seen that innovation actually gets faster when the industry comes together and aligns around an open infrastructure and shared technology standards. And AMD is the only company delivering openness across the full stack. That's hardware, software and the broader solutions ecosystem. Our software strategy starts with ROCm. ROCm is the industry's highest performance open software stack for AI. We have [indiscernible] support for the most widely used frameworks, tools and model hubs, and it's also natively supported by the top open source projects like PyTorch, LLM, SG Lang, Hugging Face and others that are downloaded more than 100 million times a month and run out of the box on Instinct, making it easier than ever for developers to build, deploy and scale on AMD.

One of the exciting AI companies using AMD and ROCm to power their models is Luma AI. Please join me in welcoming the Luma AI CEO and Co-Founder, Amit Jain, to the stage. Hello, Amit, how are you? It's great to have you here with us.

You're doing some incredible work in video generation and multimodal models. Can you tell us a little bit about Luma and what you're doing?

Absolutely. Lisa, thank you so much for having me here. Of course. Luma's mission is to build multimodal and general intelligence. So AI can understand our world and help us simulate and improve it. Most AI video and image models today, they're in early, early stages, and they're used to generate pixels. They're used to produce pretty pictures. What is needed in the world are more intelligent models that combine audio, video, language, image all together.

So at Luma, we are training these systems that simulate physics, causality, are able to go out, do research called tools and then finally, render out the results in audio, video, image, text, whatever is appropriate for the information that you're trying to work with. In short, we are modeling and generating worlds. So as an example, let me show you some of our -- some results from our latest model, Ray3. By the way, Ray3 is the world's first reasoning video model. So what it means is it actually is able to think first in pixels and [indiscernible] and decide whether what it's about to generate is good. And it's also the world's first model that can generate in 4K and HDR. So please take a look.

[Video Presentation]

Ican say Amit, that looks pretty incredible. So tell us, how are customers using Ray3 today?

So we are working with very large enterprises as well as individual creators across the spectrum, and we work with them in advertising, media, entertainment and industries where like you want to tell your story. 2025 was the year when they started to deploy our models and experiment with them. And towards the end of it, we are seeing large-scale deployments where people are using our models for as much as actually making a 90-minute feature-length movie.

What customers are also asking us a lot for as they're using it more and more is control and precision. How can they get their particular vision out onto the screen. And what we have realized and through our research is that control comes from intelligence, not just better prompts. You can't keep like typing in again and again and actually do those things. So we have built a whole new model on top of Ray3 called Ray3 Modify that allows you to edit the world. So let me show you actually what that looks like. This won't have audio, and I'm going to tell you a little bit about what you're seeing.

So what's playing on the screen is a demo of Ray3's world editing capabilities. It can take any real or AI footage from cameras or footage that you generated and change it as little or as much as you want to realize the creative goals. It's a powerful system that we have developed for our most ambitious customers who are most demanding, and they spread the [indiscernible] across entertainment, advertising, and this has allowed us to enable a new era of hybrid human AI productions. The human becomes the prompt through motion, timing and direction like, you act it out and then the model can produce it. What that means in practice is that filmmakers and creators can create entire cinematic universes now without elaborate steps and then edit and modify anything to get to the result they want. This has never been possible before.

But in 2026, we are focused on actually going much further. 2026 will be the year of agents where AI will be able to help you to accomplish more of the task or hopefully, the full end-to-end of the task rather than doing some patchwork. So our teams have been working diligently building the world's most powerful multimodal agent models. Using Luma, will suddenly feel like you have a large team of capable creators who are working with you in your creative pursuit. I want to show you a brief demo of what that would feel like.

So what you're seeing here is a new multimodal agent that can take a whole script from -- of ideas with like characters and everything and start imagining that in front of you. Now this is not script to movie. This is human AI interaction. And our next generation of models provide the ability to analyze multiple frames, long-form video and make selection and maintaining the fidelity of the character scenes and story and only editing when it's needed. Here, you're seeing human and AI collaborate in designing characters, environments, shots and the whole world.

And with our agents, we believe that creators will be able to make entire stories, what used to take a large production before. Again, this has never been possible before, and we have been using it heavily internally, and we couldn't be more excited. Individual creators or small teams that suddenly have the power of doing what entire Hollywood studios do.

That's pretty amazing. It's really nice to see how these Luma agents come together and make this happen. Now I know you have a lot of choices in compute. And when we first started talking, actually, you called me and said, you needed compute, and I said, I thought I could help. Can you tell us a bit about why you chose AMD? And what has your experience been?

Yes. We bet on AMD very early on. That call was in 2024, early 2024. And since then, our partnership has grown into a large-scale collaboration between our teams, so much so, that today, 60% of Luma's rapidly growing inference workloads actually run on AMD cards. Today, we also -- so initially, when we started out, we used to do a bunch of engineering. But today, we are at a point where most -- any operators, most any workloads that we can imagine run out of the box on AMD. And this is huge props to your software teams and the diligent work that is going into the ROCm ecosystem.

We are building multi-modal models. And actually, these workloads are very complex compared to text models. One example of that is these consumes hundreds of times, thousands of times more tokens. A video that you saw, a 10-second video, it's about 100,000 thousand tokens easily. Compare that to a response from an LLM, it's about 200 to 300 tokens. So when we are working with this much information, TCO and inference economy is absolutely critical to our business. Otherwise, there's no way to serve all the demand that is coming our way.

Through our collaboration with the AMD team, we have been able to achieve some of the best TCO -- total cost of ownership that we have ever seen in our stack. And we believe as we build these more complex models that are able to do auto regressive diffusion and that are able to do text and image and audio and video all at the same time, this collaboration will allow us to significantly differentiate on cost and efficiency, which, as you know, in AI is a big deal.

So through this collaboration, we have developed such a degree of confidence that in 2026, we are expanding our partnership to a tune of about 10x what we have done before. And these 455 cards, and I cannot be more excited for mi455X because this -- the rack scale solution and the memory and the infrastructure that you're building is essential for us to be able to build these [indiscernible] simulation models.

Well, we love hearing that, Amit. And look, our goal is to deliver more powerful hardware. Your goal is to make it do amazing things. So just give us a little brief view of what do you see customers doing over the next few years that just isn't even possible today?

Right. So as Greg was mentioning, early on in LLM land, right, like in 2022, 2023, they were great for writing copies, small e-mails, things like that. We could have never imagined that we would actually put these models into real-time systems, into health care and these kind of things through accuracy and architecture and scaling, LLMs have now gotten to that point. Video models are currently in that early stage. So they're great for generating video and 3D pictures. But soon, by scaling these models up, by improving the accuracy and data, we would end up with a place where these models will help us simulate real physical processes in the work, like CAD, architecture, fluid flows, help us design entire rocket engines, plan cities. And this is not [indiscernible]. This is what we do today manually with big giant teams in simulation environments.

These models will allow us to do that and automate that to a great degree. And as they become more and more accurate, multimodal models is what we need for the backbone of general purpose robotics. Your home robot will run hundreds of simulations in its head in image and video and then try, how do I do this? How do I solve this, so that it's able to do a lot more than current generation of LLM and VLM robots are able to do. This is how the human brain works. Humans are natively multimodal. Our AI systems will be as well.

That sounds wonderful, Amit. Look, thank you so much for being here today. Thank you for the partnership, and we really look forward to all that you're going to do next.

Thank you so much.

Thank you. So you've heard from Greg and Amit, what they said is they need more compute to build and run their next-gen models, and it is the same across every single customer that we have, which is why the demand for compute is growing faster than ever. Now meeting that demand means continuing to push the envelope on performance far beyond where we are today.

Mi400 series was a major inflection point in terms of delivering leadership training across all workloads, inference, scientific computing, but we are not stopping there. Development of our next-gen mi500 series is already well underway. With mi500, we take another major leap in performance. It's built on our next-gen CDNA 6 architecture manufactured on 2-nanometer process technology and uses higher speed HBM4E memory. And with the launch of mi500 in 2027, we're on track to deliver 1,000x increase in AI performance over the last 4 years, making more powerful AI accessible to all.

So with that, let's -- so with that, now let's shift from the cloud to the devices that make AI more personal, PCs.

[Video Presentation]

So for decades, the PC has been about a powerful device, helping us be more productive, whether at work or at school. But with AI, the PC has become not just a tool, but it's a powerful essential part of our lives as an active partner. It learns how you work and it adapts to your habits, and it can help you do things faster than you've ever expected, even when you're offline. AI PCs are starting to deliver real value across a wide range of everyday tasks from content creation and productivity to intelligent personal assistance. Let's just take a look at a few of the AI PC applications today.

Starting with content creation. These videos were created from simple text prompts on a Ryzen AI Max PC, so not in the cloud, but in a local environment. Anyone can generate professional quality photos and videos in minutes with no design expertise. Microsoft has been a key enabler of AI PCs, helping bring next-generation capabilities directly into our productivity tools. For example, managing your meetings, summarizing meetings, summarizing e-mails, quickly finding files that you need, using real-time translation on video conferences. And with Microsoft Copilot, advanced AI capabilities are being built directly into the Windows experience to complete tasks faster. You just describe what you need and the PC takes it from there.

Now at AMD, we saw the AI PC wave early, and we invested. That's why we've led every inflection point. We were the first to integrate a dedicated on-chip AI engine in 2023 and the first to deliver Copilot Plus x86 PCs in 2024. And with Ryzen AI MAX, we created the first single-chip x86 platform that could run a 200 billion parameter model locally. And now we're extending that leadership again with our next-gen Ryzen AI notebook and desktop processors. So today, I'm proud to announce the new Ryzen AI 400 series, the industry's broadest and most advanced family of AI PC processors.

Ryzen AI 400 combines up to 12 high-performance Zen 5 CPU cores, 16 RDNA 3.5 CPU cores, our latest XDNA 2 NTU delivering up to 60 [indiscernible] of AI compute and support for faster memory speeds. These flagship Ryzen AI mobile processors deliver significantly faster content creation and multitasking performance compared to the competition. Now there's a lot of excitement for the Ryzen AI 400 series. And if you're walking around CES this week, you're going to see many notebooks launching this week. The first Ryzen AI 400 series PCs begin shipping later this month with more than 120 Ultra-thin, gaming and commercial PCs launching throughout the year from every major OEM across every AI PC form factor.

Now powering the next generation of AI PC experiences takes more than just hardware. It takes smarter software with models that are lighter, faster and can run directly on device. These are different than what you're seeing in the cloud. So to talk more about this next wave of model innovation, please welcome Ramin Hasani, Co-Founder and CEO of Liquid AI.

Ramin, it's great to have you here. I'm very excited about the work that you guys are doing at Liquid. You are really taking a different approach to models. Can you talk a little bit to the audience about what Liquid is doing and why it's different from others?

Absolutely, Lisa. It is great to be here. We are a foundation model company spun out of MIT 2.5 years ago. We're building efficient generative AI models that can run fast on any processor inside and outside of data centers. We designed from scratch multimodal models with a hardware-in-the-loop approach that allows us to optimize neural architectures for a given hardware. We are not building transformer models. We are building liquid foundation models. Powerful, fast and processor-optimized generative models. The goal is to substantially reduce the computational cost of intelligence from first principles without sacrificing quality.

That means Liquid models deliver frontier model quality right on a device. Device could be a phone, it could be a laptop, could be a robot, could be a coffee machine and could be an airplane. Basically, anywhere compute exists with three value propositions: privacy, speed and continuity. It can work seamlessly across online and offline workloads.

Ramin, our teams have been working really closely on bringing more capable models to AI PCs. Can you share a bit about that work?

Absolutely. Today, I've got 2 new product announcements. One, we are excited to announce Liquid Foundation Models 2.5, the most advanced tiny class of models on the market. At only 1.2 billion parameters, the model performs best on instruction-following capabilities between its class and models that are larger in its class. LFM 2.5 instances are the building blocks of reliable AI agents on any device.

To put this in perspective for you, this model delivers instruction following capabilities better than the DeepSeek models and Gemini Pro kind of models, Gemini 2.5 Pro right on the device. We are releasing 5 model instances, a chat model, an instruct model, a Japanese enhanced language model, a vision language model and a lightweight audio model, audio language model basically. These are highly optimized for AMD, Ryzen AI, CPUs, GPUs and NPUs. And today, they are available for download on Hugging Face and on our own platform, Leap. Here you can enjoy them.

That's pretty cool.

So we can stack the [indiscernible] 2.5 instances together to build agentic workflows. But then it would be really amazing if we can bring in all these modalities into one place. So that brings me to my second announcement. LFM 3. LFM 3, it is designed natively multimodal to process text, vision and audio as input and deliver audio and text as an output in 10 different languages with sub-100 millisecond latency for audiovisual data. You will get LFM 3 later in the year.

All right. That's fantastic. So now I mean, help our audience understand like why should they be so excited about LFM 3? Like what can we do with these models on an AI PC?

Absolutely. So most assistants, AI assistants, Copilots today are reactive agents. You open an app, then you ask a question, it responds. But when the AI is running fast on the device and is always on, it can be working on the tasks proactively for you. The task can be done in the background. So let me show you a quick demo, a reference design to inspire what is possible to build on PCs with LFM instances. Let's jump in.

Imagine you're a sales leader working on your AMD Ryzen laptop with LFM 3 backbone proactive agents activated. You're in full focus mode, working on a spreadsheet, notifications start piling up, you get a calendar notification for a sales meeting, but want to continue your work in deep focus. A Liquid proactive agent notices the meeting and offers to join on your behalf. You allow the agent to join. And while you focus on your data analysis task in the background, the meeting is in progress with your agent representing you.

Are you sure we can trust this agent?

I think. I'm a little worried there. This system can actually transcribe more than transcribing your systems and really understanding what is going on. And the system can also be hooked up to your e-mail platform. It can analyze your e-mails as your e-mails are actually coming out, it can perform a deep research functionality.

So with the deep research functionality, you can analyze every e-mail and draft the response for you, again, everything under your own control. You're not going to go -- this is not going to go wrong. Everything is offline locally on the device. So this system can deliver a summary and can do the jobs better than what you have expected, what you have seen from reactive agents. I think this year is going to be the year of proactive agents. And I'm very excited to announce that we are bringing -- we are working or collaborating with Zoom to bring these features to the Zoom platform actually.

That's fantastic. Ramin, we're really excited about what you're doing. I think you've just given people just a glimpse of what we can do when we bring true AI capability to our PCs. So thank you. We're excited, and we look forward to all we're going to do together.

Thank you so much. Thank you for having me.

Thank you.

So now you see a little bit about what's possible with local AI. But the latest PCs aren't just running AI apps. They're actually building them. That's why we've created Ryzen AI MAX, the ultimate PC processor for creators, gamers and AI developers. It's the most powerful AI PC platform in the world with 16 high-performance Gen 5 CPU cores and 40 RDNA 3.5 GPU compute units and an XDNA 2 NPU delivering up to 50 [indiscernible] of AI performance, all connected by a unified memory architecture that supports up to 128-gigabytes of shared memory between the CPU and GPU.

In premium laptops, Ryzen AI Max is significantly faster in both AI and content creation applications compared to the latest MacBook Pro. In small form factor workstations, Ryzen AI Max delivers comparable performance to -- at much lower price than NVIDIA's DGX Spark, generating up to 1.7x more tokens per second per dollar when running the latest GPT-OSS models.

And because Ryzen AI Max supports both Windows and Linux natively, developers maintain full access to their preferred software environment, tools and workflows. Now there are more than 30 Ryzen AI Max systems in market today with new laptops, all-in-ones and compact workstations launching at CES and rolling out throughout the year. But our mission is to advance AI everywhere for everyone.

The truth is there are AI developers, many of you in this room, who want access to platforms that enable you to develop on the fly. So we took this one step further. Today, I'm excited to announce the AMD Ryzen AI Halo, a new reference platform for local AI deployment.

Now I would say this is pretty beautiful. Do you guys agree? So let me tell you what it is. This is the smallest AI development system in the world, capable of running models with up to 200 billion parameters locally, not connected to anything. It's powered by our highest-end Ryzen AI Max processor with 128 gigabytes of high-speed unified memory that is shared by CPU, GPU and NPU. This architecture accelerates system performance and makes it possible to efficiently run large AI models on a compact desktop PC that fits in your hand.

Halo supports multiple operating systems natively, ships with our latest ROCm software stack, comes preloaded with the leading open source developer tools and runs hundreds of models out of the box. And this really gives developers everything you need to build, test and deploy local agents and AI applications directly on the PC. Now for all of you who are wondering, Halo is launching in the second quarter of this year, and we can't wait for folks to get their hands on them.

So now let's turn to the world of gaming and content creation. A few gamers out there. I think there are a lot of gamers out there. Look, every day, gamers and creators rely on AMD across Ryzen and Radeon PCs, [indiscernible] workstations and consoles from Sony and Microsoft to deliver tens of billions of frames. And while the visual quality of those frames has advanced dramatically over the years, the way we build those worlds really hasn't. It still takes teams months or even years to bring a 3D experience to life. Now AI is really starting to change that. To show what's next in 3D world creation, I'm honored to introduce one of the most influential figures in AI. Known as the Godmother of AI, her work has transformed how machines see and understand the world. Please welcome the Co-Founder and CEO of World Labs, Dr. Fei-Fei Li. Fei-Fei, we are so excited to have you here. You've been one of the leaders shaping AI for decades. Can you just give us a little bit of your perspective? Where are we today? And why did you start World Labs?

Yes. First of all, thank you, Lisa, for inviting me to be here. Congratulations to all the announcements. I can't wait to you some of them. So it's true that there has truly been great breakthroughs in AI progress in the past few years. And as you said, I've been around the block for a while for more than 2 decades, and I really cannot be more excited than now by where things are going. So in the past few years, language-based intelligence in AI technology really has taken the world by storm. We're seeing the proliferation of all kinds of capabilities and applications.

But the truth is there's a lot more than just language intelligence. Even for us humans, there's more than passively looking at life in the world. We are incredible spatial intelligent animals. And we have profound capabilities that use our own spatial intelligence that connects perception with action. Think about all of you being here, how you break through airports this morning, I'm one of them, or woke up in your hotel room and get to the nice coffee shop or find your way in this maze in Vegas to be here. All this requires spatial intelligence.

So what excites me is that there's now a new wave of Gen AI technology for both embody AI and generative AI that we can finally give machines something closer to the human level spatial intelligence. It's the ability to not only perceive but create 3D or even 4D worlds, reason about objects and people and imagine entirely new environments that still abate the laws of physics and dynamics in worlds, virtual or real. So that's why I started World Labs. I really want to bring spatial intelligence to life and deliver value to people.

I remember the first time I talked to you about your concept for World Labs and your passion about what this could bring. Tell us a little bit about what your models do? So the audience gets a feel for what does this really mean?

Yes. Well, I heard that there are gamers out there. So this is very exciting. So traditionally, building 3D things requires laser scanners or calibrated cameras or hand-built models using pretty sophisticated and complicated software. But at World Labs, we're creating a new generation of models that can use the recent Gen AI technology to learn the structure, not only to flat pixel structure, I'm talking about 3D, 4D structure of the world directly from data, a lot of data. So give the model a few images and even one image, the model itself can fill in the missing details, predict what's behind objects and generate rich, consistent, permanent navigable 3D worlds.

So what you're seeing here on screen is a hobbit world that's created by our World Labs model called Marvel. We just give it a handful of images, and they created these 3D scenes that are persistent and you can navigate, you can even see a top view. And our system transformed a few visual inputs into a fully [indiscernible], expensive 3D world. And it shows how these models not just reconstruct the environment, they really imagine cohesive worlds, wonderous world. And once these worlds exist, they flow together and allowing effortless transition from one environment to the next and scaling into something much larger. And this is much closer to how humans piece together a place from a few glances.

Well, it looks pretty amazing that you can do that with such little input. Now can you just show us a little bit about how the technology works?

Yes, definitely. Let's just ground it in real world, a bit more from the hobbit world. And let's do something that you're very familiar with. So over the break, our team went to AMD's Silicon Valley office. I hope they got your permission.

They did not, but that's okay.

Okay. Well, now here we are. We just use some regular phone cameras. There's no special equipment, just phones to capture a few images. And then we put them into World Lab's generative -- 3D generative model called Marvel. And then our model that can use AMD's mi325X chip and the ROCm stack software stack can create a 3D version of that environment, including window, stores, furniture size and sense of depth and scale. And keep in mind, you're not looking at photos, you're not looking at videos. You're looking at truly 3D consistent world.

And then our team started to have a little more fun and decided to model free for you for different design styles, right? I don't know which one you guys like the most. I personally really like the Egyptian one, but -- maybe that's because I'm going there in a few months.

And while this transformation is keeping the geometric consistency and the 3D inputs. So you can imagine this can be such powerful tools for many use cases, whether you're doing robotic simulation or game development or design. This can -- what would traditionally take months to do in a typical workload, we really could do it in minutes now. And we can even navigate into an entirely different world, like actually the Venetian Hotel. We just did that yesterday by taking the picture and then put it in the model and then just had a little fun and they turned this whole place into a 3D imaginative space.

Now I'm sure you guys can take pictures and send it to Marvel and experience this yourself. But what you don't see here behind the scene is how much computation is happening and why inference speed really matters. The faster we can run these models, the more responsive the world becomes, instant camera moves, instant edits and a scene that stays coherent as you actually navigate and explore. And that's what's really important.

Fei, I think you're going to have a few people going out to your website to try Marvel. Keep the server up. But look, that looked really amazing. Can you just share a little bit about your experience working with AMD and our work on Instinct and ROCm?

Yes, of course. And even though we are old friends, our partnership is relatively new. And I got to be honest, we're -- I'm very impressed by how quickly this came together. Our -- part of our model is a real-time frame generative frame model. It was running on mi325X in under just a week. And then with AMD Instinct the ROCm, our teams were able to iterate really rapidly over a course of a few weeks to improve performance by more than fourfold. And that was really impressive.

That matters because spatial intelligence is fundamentally different from what came before. Teaching AI to understand and navigate 3D structure, have motion, understand physics requires enormous memory, massive parallelism and very fast inference. And I was seeing your announcement, I can't wait to see platforms like mi450 continue to scale, and they will give us the ability to train larger world models and just as importantly, to run them fast enough that these environments can feel alive, react instantly as the user or agents move, explore, interact and create.

No, that's wonderful. Thank you for those comments. Your team has been fantastic working together. So Fei-Fei, with all the compute performance that we're going to give you and all of the innovation in your models, give the audience a view of what to expect over the next few years?

Yes. I know. And as you know me, I don't like to hype. I think the word...

I call thunder hype. So...

No, I think we should just share what it is. It is going to be a changing world. A lot of workflows, a lot of things that were difficult to do will actually go through a revolution because of the incredible technology.

So for example, creators can now experience and create real-world things in real time, shaping what's in their mind's eye, experimenting with the space, the light, the movement as if they are sketching inside a living world. And the intelligent agents, whether it's robots or vehicles or even tools, can learn inside very rich physics-aware, digital world before they even need to be deployed into the real one, making them much safer, make the development much faster, more capable and more helpful to people.

And designers, for example, or architects can walk through ideas before anything is built, exploring form, flow, materials as navigate spaces rather than just looking at staring at static plans. So what excites me most is that this represents a shift in how AI shows up in our lives.

We're moving from systems that understand words and images passively to systems that not only understand but can help us to interact with the world.

So Lisa, what we are sharing today, which is turning a handful of images or photos into coherent explorable world in real time is not a glimpse of the distant future anymore. It is really the beginning of the next chapter. So you and I talk about this even offline. We know as powerful as AI technology is, it's also our responsibility to deploy and develop it in ways that reflect true human values, that augments -- that augment human creativity, productivity and our care for each other while keeping people firmly at the center of this story, however powerful technologies are. And I'm very excited to partner with AMD and with you on this journey.

Fei-Fei, I think I speak it for everyone. You are really an inspiration to the AI world. Congratulations on all the great progress, and thank you for joining us tonight.

Thank you, Lisa. Okay.

Next shift. Now let's turn to the world of health care.

[Video Presentation]

So look, as you saw in that video, AMD technology is already at work across health care. This is one of the most meaningful applications. You've already heard about some of the stories tonight of how high-performance computing and AI is one of the areas that I am most personally passionate about, is how you can bring health care there. There's nothing more important in our lives than our health and the health of our loved ones and using technology to improve healthcare outcomes means we measure progress in terms of lives saved.

I'm very happy to be joined tonight by three experts who are leading the way in applying AI to real-world health care challenges. Please join me in welcoming Sean McClain, CEO of Absci, Jacob Thaysen, CEO of Illumina, and Ola Engkvist, Head of Molecular AI at AstraZeneca.

All right, guys. Thank you so much for being here. You can see there's a lot of excitement about healthcare. Thank you for the tremendous partnership. Sean, at Absci, you're using generative models and synthetic biology to design new drugs from scratch. Can you walk through a little bit of how that works?

Yes. Thank you so much for having us here today. Biology is hard. It's complex. It's messy. Drug discovery and development is this archaic way of going about discovering drugs. Ultimately, it's this trial and error process where you ultimately are searching for a needle in the haystack. But with generative AI and what we're doing at Absci, you're actually able to start creating that needle and being able to actually engineer-in the biology that you want, being able to go after the diseases that have large unmet medical need and being able to have the manufacturability, the developability that you want in the drug.

We're actually able to start having precision engineering now because of AI with biology. Just like Apple is engineering an iPhone or you all are engineering the 455s, we're able to start engineering biology. And what is that actually doing? It's allowing us to start tackling some of the hardest, most challenging diseases that still exist that have high unmet medical need where standard of care is poor.

And at Absci, this is exactly what we want to tackle. We want to tackle these hard challenging diseases. Two of them that we're focused on at Absci is androgenic alopecia, so think common baldness. We actually have the opportunity in the not-too-distant future to have AI cure baldness. Wouldn't that be incredible?

And not only that, be able to focus in areas that have been neglected, women's health. For far too long, women's health has been pushed aside. And we have a drug that we are developing for endometriosis that affects 1 in 10 women with the opportunity to potentially deliver a disease-modifying therapy for these women. This is what AI and drug discovery is all about. And this wouldn't be possible without the compute partnership that we've had with AMD.

Lisa, you and Mark Papermaster invested in Absci roughly a year ago. And within that year, we've been able to scale the inference and be able to, in a single day, screen over 1 million drugs in one single day. That's incredible. And additionally, we're getting on to the 355s and the memory there is going to allow us to contextualize the biology in a way that we haven't been able to before and ultimately create better models for drug discovery. The future is really bright in AI and drug discovery.

That's fantastic, Sean. Well, look, thank you for the partnership. We're really excited about all the work we're doing together. Now Jacob, Illumina is really a leader in leading and understanding the human genome to improve health. How is AI helping in your work? And talk a little bit about what the impact is for the future of precision medicine?

Yes, absolutely. I'm super excited to be here, and we definitely share a deep passion for impacting health. So looking forward to everything we can do with the two companies together, both what we have done and what we're going to do. But -- and of course, Sean, I'm rooting for that drug. So let me talk a little bit about Illumina.

We are the world leader in DNA sequencing. And DNA, as you know, is the blueprint of life, which makes all of us unique. And therefore, it's essential to be able to measure that, to prevent, diagnose and treat diseases. In a simplified way, you can think about the human genome at 3 billion letters. So that actually is like a book with 200,000 pages in. And that is in each of our cells. Now if there's just one mistake, spelling mistake in that book, that can actually mean the difference between a long and healthy life and a short and terrible life. So accurate DNA sequencing is extremely important, but it's super data and compute-intensive. In fact, we are generating in our sequences more data than is generated on YouTube every day. And therefore, the relationship with AMD is super important.

We are using your FPGA and EPYC processors in our sequences every day, and that's the only way we can compute all that and translate that into insights. Over the past decade, our technology has already now been used, as we talked about in drug discovery, but also impacting healthcare. Today, it's used for profiling terrible diseases like cancer and inherited diseases. And it's really very important to make sure and we are impacting a lot of people's health out there and have saved a lot of millions of people's lives. But we're just getting started. But biology is super complex.

So now -- and our brain can't really complement all that. But the combination of using generative AI, genome, proteomics together is poised to completely change our understanding of biology over the next period of time. It will impact drug discovery, but it will also impact on how we prevent and treat early diseases. So really, it will change our way we think about longevity and healthier life. And we can only do that with the collaboration between us and all of us on the stage and the whole ecosystem. So really excited about that.

That's fantastic, Jacob. And Ola, at AstraZeneca, you're scaling AI across one of the largest drug discovery pipelines there is. Talk about how AI is changing the way you develop new medicines?

Okay. Thanks, Lisa. And also thanks for the invitation.

So say at AstraZeneca, we really apply AI end-to-end from early drug discovery to manufacturing to health care delivery. And for us, AI is not only about productivity, it's a lot about innovation, how can we work in a different way, how can we do new things with AI.

And one area that I'm personally very passionate about is how can we deliver candidate drugs quicker with the help of generative AI. So how we're working there is that we train our generative AI model on all our experimental data that we have generated over several decades and then we use those models to assess -- virtually assess in the computer, which hypothesis, which ID for candidate drugs might work or not, and then we can assess millions of different potential candidate drugs. And then you take the best, only the one that we think is really good into the experimental lab and really validate the hypothesis there.

So we use our generative AI model to generate candidate drugs, to modify them, to optimize to really reduce the number of experiments we need to do in the laboratory. And we are applying a new way of working to the whole AstraZeneca small molecule pipeline, and we see that we can deliver candidate drugs 50% faster with the new way of working and also improve clinical success later. And we can't do that alone. We need to do that in a collaboration. So we collaborate with academia with AI start-up and with companies like AMD. And one very important area for us is hyperscaling, because we have a lot of great data, and we really want to create the most optimal best models we can. And there, we work in a collaboration with AMD to basically scale our drug discovery and [indiscernible] flow so you can handle this large new data set. So basically, we optimize the whole work with the help of AMD.

That's fantastic, Ola. Look, all of your stories are really amazing, and we're thrilled to be working with you to bring these things to life. Now let's wrap up and think about what's the one thing each of you are most excited about when it comes to how AI will improve health care? And maybe, Jacob, we'll start with you.

Yes, I'm just excited about the time we are in. This is the first time that you have technology can create massive amount of data, the first time you have the compute power and the generative AI models. That will truly change our understanding on, as I mentioned before, biology that will be translated into huge impact on health care.

Ola?

So I think with AI, we can really transform the understanding of biology. So we can go to not only to treat diseases, but we should have the ambition as a community that in the future, we can prevent chronic diseases.

That's fantastic. Sean, bring us home.

Absolutely. So to wrap a little bit on what Ola said, I want to live in a world where we can interact with people before they get sick, where we can provide drugs and treatments to allow them to continue to live their healthy life, where they're metabolically healthy. They have a full head of hair. And they have that vitality that we all look for, being able to go from sick care to preventative care to ultimately regenerative biology and medicine, where aging no longer is linear. That's the world that I want to live in that AI is going to help us create. It's an exciting time.

I think we can all say, Sean, we are super inspired. I mean, look, this is what I heard. We should expect AI should help us predict sickness, prevent sickness and personalize treatments such that we can get -- really extend lives. And you guys are really at the forefront of it. So it is our honor to be your partner. Thank you each for joining us today, and we look forward to really moving this frontier forward over the next few years together.

All right. Now we're entering the world of physical AI. This is where AI enters the real world, powered by high-performance CPUs and leadership adaptive computing that enables machines to understand their surroundings and take action to achieve complex goals.

At AMD, we spent more than 2 decades building the foundation of physical AI. Today, AMD processors power factory robots with micron-level precision, guide systems that inspect infrastructure as it's being built and enables less invasive surgical procedures that speed recovery times. And we're doing it together with a broad ecosystem of partners. Physical AI is one of the toughest challenges in technology. It requires building machines that seamlessly integrate multiple types of processing to understand their environment, make real-time decisions and take precise action without any human input. And all of this is happening with no margin for error.

Delivering that kind of intelligence takes a full stack approach, high-performance CPUs for motion control and coordination, dedicated accelerators to process real-time vision and environmental data and with an open software ecosystem, developers can move fast and seamlessly across platforms and applications. Now seeing is believing. So to show how some of this work is unlocking the next generation of robotics, please welcome CEO and Co-Founder of Generative Bionics, Daniele Pucci to the stage.

Hello, Danny. It's great to have you. Your team is doing some amazing work. Can you just give us some background about what you're doing?

Lisa, Generative Bionics is the industrial spinout of more than 20 years of research in physical AI and biomechanics at the Italian list of technology. But when we look back, actually, everything started from a simple but profound question. If an artificial agent needs to understand the human world, doesn't it need a human-like body to experience it. To answer this, we built some of the most advanced human platforms in the world. iCub for cognitive research, then ergoCub for safe industrial collaboration, and then we built iRonCub, the only jet-powered fly human robot in the world.

Throughout the process of building these robots, however, Lisa, there has been one belief that has never changed. The real working technology is that one that amplifies human potential and that is built around people, not the other way around. Now this belief has become the mission of generative bionics, but to make it real, we need compute that is fast deterministic and local. A human, for instance, such balancing say loops cannot wait for the cloud. That's why our collaboration with AMD is so fundamental. AMD, in fact, gives us a unified continuum from embedded edge platforms such as Ryzen AI embedded and [indiscernible] AI Edge running physical AI on the robot to AMD CPUs and GPUs, powering simulation, training and large-scale development. So Lisa, one compute architecture from one partner end-to-end.

I like that. I like that a lot. Now let's talk a little bit about your philosophy and approach to how are you building these things? And what are your use cases?

We think Lisa that now human robots has to be elevated to another level. So our approach to physical AI is to build a platform around the humanoid robot. And then the platform is designed to achieve human level intelligence, safe physical human robot interaction and engineered into real products. Now let's start from the robot here, we are really spired by biomechanics.

In fact, if you look at human movements, they rely on fast reflexes. We walk by falling forward and our nervous system basically exploits our biomechanics. So we are exploiting the same principles into our humanoid robots. Then humans basically learn also through touch which is a primary source of intelligence. So we believe that human robots really need the sense of touch. And finally, let's talk about the platform. So we are developing an open platform around the humanoid robot to enable the next generation of humanoid robots. So just to give you an example, the same tactile sensors that we used for the human robot are basically used also into a sensorized shoe that is being used in health care to help patients recover better and faster. But more importantly, the shoe acts as another robot sensor so that the robot has the feeling of whether or not and how to help the patient.

So Lisa, we are not building a robot. We are not only building a robot. We are not only building a product. We are building basically a platform to close the loop between humans and humanoid robots and enabling what we call human-centric physical AI.

That's super cool, Danny. Now we are at CES. People like to see things. So what exciting news do you have for us?

So Lisa, we focused on a new product identity. And our first human drop design basically that defines our DNA in terms of products, GENE.01. And I'm really happy to say that GENE.01 today has been ready to be released right now.

[Video Presentation]

Is this gorgeous or what? Danny tell us about GENE.01?

So our vision is a future where humans remain in the center, supported by technology. That's why we focus on building human robots that people can trust and accept. For us, accessibility means beauty, grace and safety. GENE.01 is Italian by design.

Is it really Italian?

Yes. But basically, what really sets GENE.01 apart is touch. A [indiscernible] skin across the robot body allows GENE.01 to feel pressure contact intention making and intention, making touch a primary source of intelligence. Just to give you examples why this is so important.

In factories, touch makes possible basically to allow human robot collaboration. And in healthcare, that is going to be pivotal. Basically, a patient can hold the robot and he can feel how to help the patient in the best way. So this enables safer decisions and more natural interaction in the real world powered by AMD computing platforms. Our first commercial human will be manufactured in the second half of 2026. And we are already working with industrial partners, including a leading steel manufacturers to deploy these robots in safety critical environments. Lisa, this is not science fiction, and we are making it happen. Thanks to you.

Thank you so much, Danny. This is truly exciting. We are super excited about what GENE.01 can do. Thank you for being here.

Thank you.

Okay. Now let's turn to one more demanding environment for robotics and automation, and that is space.

[Video Presentation]

Technology is powering critical space missions today from delivering satellite Internet connectivity to remote communities to enabling autonomous exploration of Mars, the moons of Jupiter and beyond.

On Mars, AMD Adaptive Computing enables the Perseverance Rover to operate autonomously. That same technology is also powering robotic systems at NASA, JPL and both the European and Indian space agencies, delivering reliable compute in some of the harshest and most unforgiving environments. One of the leaders in space exploration and a company using AMD technology to help build the next generation of spacecraft and lunar infrastructure is Blue Origin.

Please welcome John Couluris, Senior Vice President of Lunar Permanence at Blue Origin to the stage. John, thank you so much for being here. Blue Origin is doing just some amazing things. Talk to us a little bit about your mission and what you're working on?

Yes. Thank you, everyone, for having me here. I'm very excited to tell you about what we're doing. Jeff Bezos, our founder, likes to say that Earth is the best planet in the solar system. It's sustained life for millions of years. And as we explore the solar system, earth will be the origin of that life, that pale blue dot, that is Earth. And that's why our company is named Blue Origin.

To protect that planet, we want to move heavy industry eventually off the earth. As we look to build things such as solar power satellites in low earth orbit, settle the moon, settle Mars, explore the asteroid belt. We'll move on and we'll build that infrastructure so that eventually millions of people will be living and working in space for the benefit of earth. And that starts with one person.

Originally, we had Yuri Gagaran and Alan Shepherd explore. than the Apollo astronauts, then just recently, the International Space Station celebrated 25 years of continuous human presence in space. The next step for us is Lunar Permanence. And our business unit that I'm lucky enough to be a part of is named Lunar Permanence specifically so that everyone knows immediately what we're trying to do is establish a permanent presence of humanity on the moon. And that requires reliable, repeatable, low-cost operations and reliable and repeatable low-cost equipment and vehicles. And AMD is a critical partner of ours to make that happen.

Thank you so much for that, John. And look, talk a little bit about why high-performance computing is so important in your work and especially as your missions are getting more complicated?

Certainly. So space is the ultimate edge environment. The flight computers that we build are the heart and soul of our vehicles. That compute stack needs to be reliable, deterministic and resilient. It needs to survive the environment of space. And what that means is we have mass constraints, power constraints, radiation considerations. And the AMD embedded architecture allows us to reduce mass, save power on these vehicles and tolerate the demanding radiation environment of deep space.

And I think when we think about all of this, looking ahead, talk a little bit about how AI is playing a bigger role in your future missions?

Certainly. So AI's impact on Earth-based systems is well known. In fact, I've got to say, Lisa, and I kind of surprised you earlier today, AMD has been a phenomenal partner of Blue Origin. We, only a few months ago, started to talk to AMD about using the Versal 2 in our flight computer stack. And within a few months, the AMD team and the Blue Origin team worked tirelessly and we're able to get shipless units that we were then able to incorporate into development flight computers.

We've now, in a couple of months, built the development flight computers that are flying in our vehicle test bed, and those will eventually power our Mark II Lander, that Mark II Lander will land astronauts on the moon as early as 2028. In fact, it was so impressive. We had a team of Blue Origin engineers working over the holidays, and we took the entire flight computer stack, and we're able to successfully simulate a landing on the moon. This has saved months and months of schedule.

Now you take that to the AI use and how important it is for us. Right now at Blue Origin, AI use on earth is critical. Every employee at Blue Origin has access to AI tools, whether it be for design, for analysis, for just basic painting back and forth. AI has sped our development process so quickly that we're now looking, how do we bring this to spaceflight? And so what that means for spaceflight for us, that's the next great step, where AI becomes a complement to the astronaut, a copilot, if you will, identifying landing sites, looking out for hazards, being able to do that level of compute in a real-time environment is critically important to us.

For me personally, though, you think of edge AI. What is really interesting is as we go to explore the solar system, radioastronomy has been a passion of mine. And what radioastronomy is, is you're looking for weak signal radio frequencies that are being emitted throughout the universe. The problem we have is that Earth is a great emitter of radio frequency noise and interference. So it's hard to identify that. The far side of the moon provides a natural shelter, a barrier to that noise. So if we could land a Mark I vehicle on the far side of the moon, we could start to explore this untapped radio frequency environment.

If we have Edge AI, we can now utilize that to do the deep exploration to actually identify where we should be looking next because relay back to comms doing this latency really hurts our ability to explore. So by having it on the far side of the moon, the Mark I vehicle with Edge AI will tell us land the next vehicle here to optimize your exploration. That's really what excites me.

I mean that's super, super cool. Look, John, we're honored to work with you. I think it is an incredible mission that you have. Thank you so much for the partnership, and we look forward to what you do next.

Absolutely. Thank you very much.

Okay, guys. Now let's turn to our last chapter of the night, science and the supercomputers used for the most advanced scientific research.

We are incredibly proud of our leadership in high-performance computing, and we have continued to push the leading edge of performance here. We're actually seeing a convergence between traditional high-performance computing systems and AI as we bring together the best of both of these worlds. Today, AMD powers the 2 fastest supercomputers in the world and more than half of the 50 most energy-efficient systems. These systems are using massive amounts of compute to solve previously impossible problems. In Finland, the Lumi supercomputer has cut climate model update times by more than 85%, enabling earlier warnings and better preparation for extreme weather events.

Energy Giant [indiscernible] is using an AMD-powered supercomputer to develop longer-lasting batteries and cleaner fuels. At Oak Ridge National Labs, the world's first exascale supercomputer is running Orbit 2, a hyper-resolution global model that allows us to predict unprecedented forecasting detail with nearly 99% accuracy.

And at Lawrence Livermore National Labs, the world's fastest supercomputer, El Capitan is modeling how viruses might mutate and evolve, enabling scientists to design more resilient antibody treatments and respond faster to future pandemics. Going forward, there's a lot more that we can do to power the future of scientific discovery. We are actually working very closely with the U.S. Department of Energy and America's National Labs as part of the Genesis mission.

Genesis is a national program launched late last year to accelerate the convergence of AI, supercomputing and quantum computing. Together with Oak Ridge National Labs, we recently announced 2 new supercomputers that are part of the Genesis mission. The Luxe computer that is the first dedicated U.S. AI factory for science that will come online early this year; and Discovery, the next flagship supercomputer planned in 2028. Genesis is the most ambitious public-private technology initiative in decades. Leading this historic effort is Michael Cratios, who has shaped national policy at the highest levels as a former U.S. CTO and Under Secretary of Defense for Research and Engineering.

Please join me in welcoming the President's Chief Science and Technology Policy Adviser, Michael Cratios to the stage.

Michael, thank you so much for being here. I know just how busy you are. You've described this Genesis mission as a real moonshot with the largest mobilization of federal scientific resources in decades. Can you talk a bit about why Genesis is so important?

The Genesis mission is a great example of how President Trump has moved fast in less than a year in order for the U.S. to lean in and win the AI race. Genesis is the largest marshaling of federal scientific resources in the recent history and at a scale and an urgency as the Apollo Mission or even the Manhattan project. We are bringing together the unmatched power of our national laboratories, supercomputers and the nation's top scientific and innovative minds with the goal of doubling the productivity and impact of American science within a decade. This whole of government approach represents a historic mobilization of resources, tasking the Department of Energy to integrate its world-class supercomputers and data sets into a unified closed-loop AI platform.

Integrating this data, the Genesis mission leverages the power of AI to automate experiment design to accelerate simulations and generate predictive models that accelerate federal R&D productivity. Priority areas of focus include the greatest scientific challenges of our time that can dramatically improve our nation's economic and national security. These span biotechnology, critical minerals, nuclear energy, space exploration, quantum, semiconductors, microelectronics. And a few weeks from now, a few weeks ago, we announced the first wave of industry partnerships with Genesis Mission, and that included AMD. So thank you for that.

As a next step, we're working towards bringing even more federal resources into the Genesis mission, and this is going to include a variety of agencies, including the National Science Foundation, National Institutes for Health and the National Institute for Standards and Technology.

Well, look, thank you, Michael. We are very proud to be part of Genesis, super excited. If you look back, so many of the technologies that we have today really started with long-term public and private partnerships. So where do you see Genesis actually making the biggest impact beyond science?

Well, through Genesis, we will create the world's largest and highest quality scientific data sets to train the next generation of AI systems, pushing them beyond their current mastery of language and code into the realm of science. Now as you can imagine, this will lead to tremendous spillover effects across health care, drug discovery, energy and manufacturing. Fundamentally, we are seeing a massive shift in America science and technology enterprise. We are now at a place where the U.S. government, private sector and universities together are investing over $1 trillion in R&D every year with the private sector leading the way by carrying out 2/3 of that R&D alone. Now the Genesis mission understands this and leverages the full strength of that entire ecosystem.

That's wonderful, Michael. Now we have talked a lot about how important it is for the U.S. to lead in AI. Can you talk about what are the biggest things that we must do to get right such that we lead in AI?

Absolutely. There are three strategic priorities the U.S. needs to get right as laid out in President Trump's AI Action Plan. The first is we need to remove barriers to innovation and accelerate research and development. We are already at work looking for regulatory roadblocks innovation and seeing where we can update or remove them entirely. This effort will ensure the U.S. is a home for the next great technologies to be created and to be commercialized.

Next, we need to get AI infrastructure and energy production right. We've taken significant actions to streamline permitting to data center construction and support all forms of energy, including advanced nuclear reactors. Looking beyond our borders, it's all about AI diplomacy and exporting American technologies to the world. The U.S. government is underway in establishing the American AI Export program to bring American innovators and innovations to our partners and allies around the world.

The Department of Commerce will be issuing an RFP this month seeking proposals to create a turnkey AI stack, including everything from infrastructure and chips to models and applications. Last but not least, another strategic priority for President Trump and First Lady [indiscernible] Trump is AI and education.

I talked earlier about winning the AI race. Focusing on AI and education is about truly winning our AI future today. It starts by helping parents and teachers and students navigate AI's opportunities and challenges in the classroom. I'm thrilled that in a matter of months, we are seeing tremendous participation with over 5,000 students and 1,000 educators across all 50 states signing up for the presidential AI challenge. Now submissions closed on January 20, so please visit ai.gov to participate. And also look for these regional competitions that are ultimately going to culminate at championship at the White House this summer.

We've also secured over 200 pledges from leaders like AMD for free AI educational resources, including apprenticeships, access to AI top models and curricula for so many teachers around the country.

Michael, look, we're incredibly proud to support the AI education pledge and really to help expand access to AI education with more hands-on opportunities for students to learn and build. That's why we've actually committed $150 million to programs that bring AI into more classrooms and communities across the country. We're investing in the next generation of AI research and talent. We're building research collaborations with more than 800 educational institutions around the world, including many of the top engineering and computer programs. And we're also committing to developing coursework to promote our open ecosystem. So we're offering free online AI courses to reach over 150,000 students this year.

So Michael, I want to say thank you for your leadership on this topic and the first lady's leadership. I can tell you that it certainly is making a difference in galvanizing the industry.

Now before you go, I have a very fun thing for us to do. It is really a moment for us to highlight some really amazing work as a direct outcome of the AI education pledge. So a little bit of background. We recently partnered with [indiscernible] on a nationwide AI and robotics campaign. More than 15,000 high school students signed up with the top teams coming together in Silicon Valley last month for an in-person hackathon to bring their designs to life. It's actually incredible to see what these students were able to build in just one weekend. You can imagine it was a little bit competitive at this hackathon. And as part of the recognition, we invited the top 3 teams to be here at CES right here in the front row so they could experience the biggest tech event of the year firsthand, and we could congratulate them in person. So let's give them a big round of applause.

And to tell us a little bit more about their project, I'd like to invite the Hackathon Gold Medal winners, Emmy McDonald, Rana D Boyan and Aya Eva of Team Armtender to the stage.

All right. You guys are amazing. Congratulations on the incredible work. Now before you talk about the project, can you just share a little bit about yourselves, like where are you from? And when did you start coding?

Sure. I'm Emmy, I'm 17, and I'm from Chapel Hill, North Carolina. I started coding when I was around 12, and I joined [indiscernible] when I was 16.

Good evening, everyone. It's great to see you all. My name is [indiscernible], and I'm a 17-year-old student from Cleveland, Ohio. I started coding when I was 12, and I joined Hacklub as soon as I turned 16.

Hi, everyone. My name is Afya. I'm from [indiscernible], Wisconsin. I started coding about 2 years ago through [indiscernible].

That is fantastic. Now tell us a little bit about your project.

Of course. Together with [indiscernible] and Afya, my [indiscernible] teammates, we built an AI robot Barista. It's a robotic arm that autonomously serves beverages using a motorized wheel that sends to select a soft drink. We trained a single unified vision language model to multitask using the AMD Developer Cloud with mi300X GPUs. The robotic arm runs entirely on an AMD Ryzen AI laptop using 3 cameras. And we came to the Hackathon with no previous AI training experience.

Now can you believe that no previous AI training experience, and this is what they did.

You guys are clearly doing great things. Just give me an idea, like what are you most excited about working on next?

Thanks so much, Lisa. So my mom actually works at our local fire department. She's in the audience, I mom, and she is a former firefighter. One thing that we noticed when training arm Tender is that it was able to capture complex human behaviors, like it was able to try again to grab a can after it missed it without us programming that specifically. I want to build a robot that can be used in firefighting to go into building fires and case the building before firefighters go in. And the complexity of motion that we saw AI exhibiting could make that possible in a way that wasn't before.

I love that. What do you guys think?

Well, look, we are all about encouraging and inspiring young people to pursue their dreams. And tonight, we actually have a special surprise for you guys. So AMD is awarding each of you a $20,000 educational grant to invest in your future as innovators to help.

Thank you so much.

You guys are just a great example of what the AI education pledge is all about because what you've created, it's clear that there's so much we can do. So congratulations to all of you. And Michael, thank you for being here and really helping really bridge all of this together. We appreciate everything you're doing for the country and for the industry. Thank you so much.

Look, it's been fantastic being with you tonight, but it's time to wrap this up. So I hope you all saw tonight what I see every single day. This moment in tech not only feels different, AI is different. AI is the most powerful technology that has ever been created, and it can be everywhere for everyone. We're entering this era of yotascale computing where the deployment of more powerful models everywhere will require a massive increase in the amount of compute in the world. Meeting that demand will take a broad portfolio of solutions from the largest systems in the cloud to AI PCs to embedded computing. And just as important, it takes an open ecosystem built on industry standards.

That's what you saw on stage tonight. We wanted to bring you the entire spectrum from amazing technology to deep co-innovation with industry leaders across the ecosystem to very strong public-private partnerships. All of us are working together to bring AI everywhere for everyone. On behalf of the 30,000 AMDers around the world, we're proud to be building the future together with all of you because the world's most important challenges can only be solved by bringing the industry ecosystem together. Thank you for joining us tonight, and enjoy the rest of CES 2026.

Good to go. All right, everyone. Welcome back to the Barclays Global Tech Conference. I'm pleased to have Jean and Matt here from AMD. Thank you for joining.

Thank you. Thank you for having us.

Thank you for having us.

No problem at all.

So why don't we start with the question that's on everybody's mind, as we exit kind of 2025 and go into '26 here. There's been a ton of AI spend announced, we aggregate kind of over $3 trillion. The compute networking portion of that we can argue about all day. But I think the conversation is centered around the feasibility of actually deploying all of the spend in the time line that's been laid out. Maybe talk to me about kind of what you're seeing in terms of the ability to deploy this and then how it's benefiting AMD in general.

Yes. Thanks for the question. First is the way we look at the AI is we are really in the early stages of a multi-decade investment cycle. If you think about it, it's a very transformational technology which will change the global economy fundamentally. So it's absolutely the case that if you have a more data center, more compute, you can actually generate more intelligence and more capabilities.

The CapEx spending is super high and it's quite significant. The way we think about it is when we talk to our customers, you can see they are the ones, the hyperscale companies, they are increasing CapEx spending. And frankly, they are all very well-capitalized companies, they're funding it through free cash flow. And so the whole ecosystem is really funding the investment.

More importantly, what we hear from our customers is that they are increasingly more confident about the business model for AI, right? Not only they are seeing real workload, the cases, they can see the productivity improvement, also unit economics is also improving, inference cost base coming down.

So I think now what they're telling us is actually they are constrained by the compute, by the infrastructure. If they have more compute, they actually can support more applications, they can tie their investment with revenue, return on investment from that perspective. So we do think everybody is working very hard to bring up more capacity, which of course we provide significant compute, not only on the GPU side, but also on the CPU side. We see the tremendous demand for our compute in both accelerator side and the CPU side. I think it will benefit us from longer term.

And increasingly of very, very late, you've seen the debate shift more from general purpose silicon to "Can custom silicon scale across multiple customers?" And how does that impact general purpose silicon providers? Maybe for the both of you, just what do you think about the ability for a chip that was designed for a specific customer to be used more broadly? And when you see someone like a Google having success externally, does that -- do you feel like that cuts into your TAM? Or maybe lay out why that would be a different swim lane than what you're in today.

Yes, I'll start with a very high level, then Matt can provide more colors on. If you really think about it, AMD's view has always been we see a $1 trillion data center market opportunity, of course, majority of them are accelerator opportunity. We always said it includes both the general purpose compute and you call the ASIC or custom silicon. And we always have said the ASIC or custom silicon is going to be 20% to 25% of that market opportunity. So it's huge. That we always believed.

And we always said it's really about different compute for different workloads. But consistently, the program, both architecture we have, we can support more variation from all those workloads, the training inference, pre-training, post-training, that continues to be the flexibility customers are requesting.

Of course, there's the most recent debate about Google TPU and general purpose GPU. It has been -- we always have the same consistent view on TPU, Google, what we have done with Broadcom. Very good. But they are still very specific from workload support perspective. The customer wants flexibility overall. So majority of the market, we continue to believe, it will be general purpose GPU. I think, Matt?

Thank you, Jean. And Tom, thank you, guys, for -- everyone at Barclays, for having us here.

I think it's interesting. First, one perspective too is, from the model company's perspective, whether these are AI-native model companies like OpenAI and Anthropic and others, or whether they're hyperscale companies with their own models, that's a super competitive space in and of itself. There will be -- recently, Gemini 3 published, and it's an incredibly good model, and that got a ton of attention. Next month or the month after, another model that's trained on -- whether it's trained on ASICs or whether it's more likely trained on GPUs that will be a better model than that model. And there'll continue to be this leapfrogging and what we've observed as a big swing in sort of investor conversation around this. But you should anticipate as investors this being a continuation of these model companies getting better and better.

And as Jean said, getting the right silicon to the right type of work is super important. We've tried to architect our Instinct family as we go forward at the rack scale and MI450 to be general purpose in nature to serve all of the customers. The flagship product of that portfolio would be the MI455 that we'll ship to OpenAI and a bunch of other folks. There's also an MI430 version where we've taken the main compute chiplet out and put in a separate compute chiplet that has more floating point that's akin to what's been done in the HPC market.

So we have -- the market doesn't have to go completely GPU or completely custom. There's a lot of semi-custom opportunities in between to get the right type of silicon to do the right type of work. And I would just encourage this audience not to maybe overreact to the news of the day. This is going to be a super competitive market on the hardware side. It's going to be a super competitive market on the model side. And you're going to get new data points that come out all the time.

We've, as Jean said, we've been consistent in our own modeling inside of AMD that 75% to 80% of this market is going to be programmable load store architecture computing at the GPU level. And that's where our customers are asking us to provide consistent annual cadence system-level competition. And that's what we're going to go and do. But there's certainly a model -- there's certainly a market for ASICs. 20% to 25% of a $1 trillion TAM is a big market, and there will be folks that are very successful in doing that. So that's kind of our perspective right now.

Perfect. Yes. So take that 25% out of the pie, there's 75% left. If you look at your long-term kind of TAM, you talked about $1 trillion, NVIDIA talks about something $3 trillion to $4 trillion. Could you maybe walk through why their TAM is so much larger? Is that a function of gross margin? Is it a function of networking? What are they adding in that you guys aren't? Because you would assume you guys are probably closer apples-to-apples than those numbers would...

Yes. So let me clarify our TAM. What we are focusing on is really silicon addressable market opportunity for AMD. So our TAM, when we talk about the over $1 trillion data center TAM, we include accelerator, which is general purpose GPU, ASIC or custom ASIC or how you call it. We also include our expanded TAM on the CPU side, also networking, scale-up networking, which we also have an offering.

So those are what we focus on. We actually don't include racks. We don't sell racks. We don't include cable, all the other solutions component that build up to the rack or cluster level. Of course, we also don't include the data center infrastructure build-out. Those are not what AMD is focusing on. So of course, what the other competitors talk about their TAM, it's very different. So that is what AMD is focusing on.

Yes. Tom, I think the growth rates of the TAMs, regardless of how you define them, all those curves look very similar. We focus -- we have a data center business segment, right, that is our server CPU business, our data center AI business, our scale-up NIC business. What we tried to forecast at the Analyst Day a few weeks ago was AMD's TAM, we're not in the business of forecasting data center CapEx or NVIDIA's TAM or Broadcom's TAM or anyone else's TAM. We're thinking about our silicon TAM that we can directly address with products that AMD will and could offer. That's all we've included.

So there's certainly -- if you want to forecast data center CapEx that would include power and buildings and water and cement and all kinds of other things that AMD is never going to sell. So we just try to forecast our own TAM.

I want to move to something a bit more customer specific in OpenAI. I thought that it was really an unlocking of investors' minds when they saw the deal with OpenAI and was like, wow, this really brings AMD to the centerfold of the conversation with NVIDIA and Broadcom in terms of, one, ability to provide compute that is very, very real in the next 12 months. And then two, you had structure of the deal, which was a bit unique, but also very interesting in that your economics kind of scale with the deployments as well.

Maybe one, talk about why you structured the deal you did and the way you did with OpenAI. And then two, just judging by general math and kind of what you've said, it's about a gigawatt of deployment in the back half of next year, how ready is the ecosystem to get that out there with all the other compute announcements? And do you feel secure in your ability to get the product that you need and have those deployments go to market?

Yes. Thank you for the question. We are very pleased with the partnership with OpenAI. It is a definitive agreement, not LOI. We signed with OpenAI for 6 gigawatts over several years. I think, as you mentioned, it is a win-win situation. The framework is really based on they scale up the deployment of AMD's MI450 and the next-generation products. And at the same time, there's a performance-based warrant, is that when we ramp up our revenue, which creates value for shareholders, then they also get warrant from the partnership that we have.

So that is how it's designed. But to be clear, we have been working with OpenAI for a long time, multi-generation, starting with MI300 and then MI355, and now, trying to deploy MI450.

So the first gigawatt is a commitment. We'll start to deploy in second half of 2026, but it will ramp into 2027. And the whole ecosystem we are working with really focused on the planning from the data center, CSP selection, the power, to supply chain, our ecosystem partners to help us to ramp the MI455. Those are the overall system we have been working with partners. So we feel pretty confident about the execution part for the starting ramp for the second half and then going through 2027.

Of course, the relationship is multiyear, multi-generational. I think we are both very motivated to continue to drive the future partnership too.

Yes, we saw earlier this year at both the Analyst Day and then previously, Sam was onstage with you guys for a current period of time talking about how they were very involved in the design of this product. And then you actually got to see Helios in person. Can you talk about like where the differentiation is versus other rack architectures? And then maybe customer engagement since you've had that out there. I would assume customers get a little bit of an earlier peak than us, but something that customers are coming to you and saying, "Wow, this is really unique. We prefer this solution versus what we've seen so far."

No, it's a good question. So one of the things that we focus on really heavily with the work between AMD and OpenAI is, with them being arguably the leading model company in the world, we focused -- I mean, there were sort of weekly level executive engineering engagements back 18, 24 months, right? It wasn't like we just popped out with a product and we had an announcement.

They've had -- they, among other customers, have had influence and given us feedback on the design of the GPU itself and some work that we've done in our ROCm software stack itself. And then you think about what we're doing in the road map with the Helios rack and how we worked with Meta on that around OCP to have an industry standard compliant rack that, you might imagine, we could make more dense as we move forward because of the double-wide rack footprint.

The engagement level across the board with customers has been a very deep one. I think Lisa has talked at the Analyst Day and in other forums about having multiple multi-gigawatt engagements over the MI450 time frame. And OpenAI is a critical partner, but there will be others as well.

One of the really exciting things for us about the close, close partnership with OpenAI is that they do deploy their infrastructure in many places, with a number of hyperscalers, with a number of neoclouds. And the work that we were doing in AMD anyway on MI355, MI450, MI500 series after that, was to partner with a very wide range of customers and push our infrastructure into all of the CSPs and all of the neoclouds sort of on our own. And we were having great progress in doing that, and you saw the customers we had in our event back in June.

Now we have an additional really large customer pulling us to scale at all of those different platforms as well. And that gives a breadth of other customers' confidence that, through the partnership with OpenAI, at various places in the industry, AMD will have scaled infrastructure that we can then build our work on top of. And so the engagements with customers that were happening anyway have both deepened and accelerated in time since people have gotten a view as to what the OpenAI deal looks like and the fact that the Helios rack has been sort of unveiled to the world. So it's been an exciting 6 months and we're really pleased to move forward with the breadth of the customer base.

And then one for Jean on that same topic. You talked about over time with volume, the data center GPU business getting up to corporate gross margins and potentially in the future maybe being better. But rack-scale architecture obviously brings into account a lot of -- a variety of other subsystems, components, et cetera, that generally are a margin headwind. Can you talk about as you see Helios ramp, what that does to gross margins on the corporate level?

Yes. To be clear, we actually don't sell the Helios rack-level systems. Our focus, as we talk about our TAM, it's really silicon. It's more focused on the high value-added piece, which include GPUs, CPUs and sometimes the scale-up networking. So when you think about our business model, it's really not changing from what we do today. We really want to focus on the high value-added piece. And at the same time, we do provide reference design for our partners and we are committed to open standards so everybody can also make money. And from TCO perspective, it's better TCO for customers too.

On the gross margin, we always have been focused on, right now, the priority is the market share expansion and the gross margin dollar pool. As you can see, the market is expanding very quickly. That is what we focus on right now. So right now, the GPU gross margin is slightly below corporate average. But going forward, when we scale our business, when we really optimize the solutions for our customers, that we do think the gross margin will go up.

One thing to be clear is, right, we talked about this at our Financial Analyst Day, if you look at our strategy at company level is we are building a compute platform which including GPU, CPU, they all include adaptive compute and other solutions for different end markets. From a company level, we always leverage our investments across all the platforms.

Same thing on the gross margin side. We do have multiple drivers. We can continue to improve company's gross margin, right? On the CPU side, we're getting into commercial market, which has higher gross margin. Same thing on the client side, we see tremendous opportunities to continue to improve gross margin. And then our FPGA business is very gross margin accretive. So when we add it together, take a step back at the company level, we are driving the gross margin to be at 55% to 58% as our long-term model. And we feel very comfortable about that trajectory.

Yes. Tom, just to reiterate what Jean said at the beginning, because we continue to get some questions about this, is we are not selling racks. We are not selling servers. Our OEM and ODM partners will sell the racks and sell the servers. We will work extremely closely hand-in-hand with them through our ZT Systems services team, to we'll license the reference design, oftentimes we'll license testing and testing programs to make sure they can test the racks and deploy the racks. We'll help provision the supply chain for all of the other components, whether that's cables or connectors or power supplies or a whole laundry list of things. And we will be at AMD responsible for delivering the servers to the model company, to the hyperscalers, making sure that they run workload and that they run efficiently.

But all the other pass-through components that are not part of the silicon TAM will not run through our P&L. So just to be clear about that, because we've gotten this, as you go to rack scale, what happens to margins? But we're going to be a fabless semiconductor selling semiconductors the same way we've always been. So hopefully, that's pretty clear.

That's why we ask it onstage. All right. So next thing is NVIDIA has brought to market a CPX, which is an interesting, at least from my perspective, a new type of compute where you would imagine it'd be doing something like a prefill functionality. You're seeing this ecosystem evolve very rapidly. That to me looks more like a custom piece of silicon or a CPU in general. But like, did that design choice mean that you will necessarily fall in that direction? Is there a reason why they would go in that direction? And a better question, because you obviously don't want to talk about your competitors, is what could you guys do in next generations that that CPX chip does that would improve your kind of performance?

No, it's a good question, Tom. I think we do a ton of work on, with the customers, on workload characterization of AI workloads, right? So there's obviously this growth at different rates of prefill and decode, and they've made a certain design decision around, in certain instances, doing a dedicated piece of hardware for that. We've evaluated it extensively in the MI450 time frame, we're doing PD and software. We're not yet convinced that the relative ratios between prefill and decode and other part of the inference workload pipeline are yet fixed enough to make dedicated hardware decisions.

But we have some flexibility as well. I mentioned earlier the ability to maybe take our overall platform and substitute in different compute chiplets into the road map over time. So you don't need to do, in our architecture at least, a brand-new piece of total silicon to subsegment parts of the workload. There are certain places where pre-training and training and things are getting closer to inference. And the way the workload is characterized, there's important parts of the algorithm stack that might slow down and be more amenable to a fixed piece of silicon versus other pieces that continue to evolve very, very quickly where you want flexibility.

And so I think for us, there's -- we've not yet made that choice and we're in the current gen doing PD and software. But we're evaluating all parts of both the training, pre-training and inference software stacks as to which part might require some more general silicon and which part might require some more dedicated silicon over time, and our customers all have a view of that as well. But we've evaluated it super closely. And right now we're doing PD and software, but that may change going forward as the algorithms mature.

Another one on the technology side. Scale-up architecture is a huge debate today. You guys have been committed to UAL longer term. First generation is UAL tunneled over Ethernet. More recently, you've seen, with Amazon, T4, using NVLink Fusion, at least in some SKUs.

With you guys offering a system architecture or a footprint for others to engage with, how do you see the world evolving? Like do you think that ultimately everyone interacts with the large general purpose silicon providers in terms of back-end ecosystem? Like if you get UAL up and running, will people kind of use yours as well? How do you see the world evolving? And where do you see scale-up architectures moving in the next 3 to 5 years?

Tom, I think what we care about is driving TCO at the rack and the data center level and adding -- one of the areas that we want to support open standards, just like we do in our ROCm software stack where we provide a lot of openness to the ecosystem, is on the networking architectures we choose. For example, for scale-out networking over Ethernet, we have built into Helios the flexibility to have different switch vendors that do scale-up Ethernet.

On the scale-up domain, as you mentioned, we've been doing a technology inside of AMD for 5 or 6 generations in our server business called Infinity Fabric that's done coherency across chiplets, across sockets, across racks in our server business out to supercomputing scale. We've licensed that to the UAL consortium, that they've ratified to be 1.0 standard of the UAL standard.

In the initial implementations of Helios that are going to launch in the second half of next year, we're using that traffic that were really -- that's critical, right? The UAL traffic that's Infinity Fabric coherency traffic, that's what we're really, really focused on.

The transport layer, we're a bit more agnostic to what the customer wants to do. And there may be some, in the 2027, '28 products, there may be some opportunities for us to support native UAL silicon that can have some power and latency advantages. And I think we would expect many of our customers to adopt that because there are some technical advantages to doing that.

But if there are customers that want to continue to tunnel that traffic over Ethernet or scale-up Ethernet or other protocols, we're totally fine with that. What we want to do is make sure that the coherency works on the functional level and it's performant. And the underlying silicon transport protocol is going to be driven by the needs of the customer.

And so we have some of our own technical opinions about which one might be better than others, but that's not our business. I mean the customers are going to decide what their scale-up architecture is going to look like, and we're going to make sure that our coherency protocol is validated over whatever transport they decide to use.

So we went very deep in the tech. Pulling back out to the macro. News on China, again, over the last week, we see several iterations of this. I would say the most recent was there was some ability to sell, but it seems like customers in China were not taking that product. Maybe just, whatever, I know it's a sensitive issue, how do you feel about the current arrangement? What's changed for you? And do you think it really changes the dynamic of Chinese customers taking your product?

Yes. The situation with China probably is the most dynamic. Every day there's some news. I think we do expect, based on the most recent news on H200, we do expect we would expect to be treated the same for our MI325 product, which is similar to H200. Of course, we support the administration's effort to help the whole industry, but at the same time, they're still working through the details, so just like all the different complications with the situation in China. So on the MI325, we will apply for licenses once they work through the details. But then, as you mentioned, there's still the China customer demand question we still need to figure it out.

On MI308, as we guided Q4, we did not include any revenue from MI308 because of uncertainties we have. We did obtain a few licenses we are working with our customers on the demand side. They're just always very uncertain about what's going to come or not. So we are going to monitor the situation, make sure we're compliant, not only with the U.S. government's export control rules, but also on the China side.

Great. I want to hit a couple of rapid fire as we wind down time here. In Client, continue to see really good share gains. ASPs have been a huge positive story as the year has gone along. I actually think that ASPs have held a little bit better than even you guys have described in the back half of the year. What's driving that? And can that continue? Should we be seeing some normalization there into Q4, Q1?

Yes. First, we are very pleased with our Client business performance. If you just look at the last 3 quarters, we literally increased revenue by 60%. And the majority of them actually is driven by ASP expansion. The major reason is not only we have been going up the stack to really go to the premium PC, not only desktop side, and also on the mobile side. And secondly, we're getting into enterprise commercial market, which is also higher-margin product.

So overall, that has been our strategy. We do believe we have the best technology and product portfolio right now in the PC market. So we'll continue to drive, we should expect the consistent ASP trend, just like what we have seen in the last 3 quarters. The team is very excited not only about Q4, and the next year, how we can continue to execute to expand our market share.

And then your competitor has talked about supply tightness in Client as well as server. Are you guys seeing this as well? And is this an opportunity for you guys to gain more share? Or how do you view this dynamic?

Yes. It's a good question, I think, for 2 things. One, in the Client side, as Jean said, we're going to continue to push to gain share in enterprise, in particular, hold a very, very, very strong position we have in the premium desktop where the ASPs and margins are quite strong. And we'll certainly work as best we can to support our customers if there's any shortages in the industry. We'll have to be really strategic about that from a margin perspective, but make sure that we can step in and help the customers where needed.

And then on the server side of the business, which is something that we didn't get to quite in this conversation, we continue to see a pretty rapid expansion in our enterprise footprint. One of the statistics that got maybe overlooked with all the things that we threw at the investment community at the Analyst Day was we've expanded, almost doubled our enterprise customer count during 2025, and we'll see how the land-and-expand goes there.

And in addition, pretty much at all of our top hyperscale customers where our market share in server is fairly high, we've seen an expansion of the TAM. As those folks have deployed inference, you've seen a significant amount of additional CPU demand to support the inference traffic, whether it's agentic inference, whether it's storage servers, whether it's -- I mean, the head nodes, there are some places where people are running inference on server. Just across the board in the server portfolio, we've seen -- there was a thesis in market for some period of time that AI was going to be cannibalistic of the CPU server market, and I think we're seeing the exact opposite happen and in an accelerated way and broadening out of that trend.

So yes, the CPU portfolio, I know the shiny light of AI has got a gravitational pull to it with investors, but the underlying CPU businesses and AMD are in a great spot.

Well, we've run out of time here. I very much appreciate you both being here. Thank you so much. And it sounds like things are going quite well.

Yes. Thank you so much.

Thank you, everybody.

Thank you, Jean.

Good morning. We're going to get started here. I'm Tim Arcuri. I'm the semi and semi equipment analyst here at UBS, and we are very honored to have Dr. ls with us from AMD. So good morning, Lisa.

Good morning. Thanks for having me.

Great. Thank you. So first, I just wanted to start by talking about the transformation that you led, I think beginning 3 or 4 years ago. You've transformed the company from being less than 20% data center to nearly 50% this year. What have been the drivers for this transformation? Some of it has been market growth, but some of it was a decision that you made years ago to sort of pivot the company in this direction.

Well, again, Tim, thanks for having me. It's great to be here with everyone. And I think, in the technology sector, it's all about making the right big bets when you look at what the inflection points. I think over the last, let's call it, 5-plus years, we've been incredibly focused on high-performance computing as a sector, knowing that, as we go forward, compute would be such an important part of unlocking capability and intelligence. And then a few years ago, it became absolutely clear that this was going to be all about AI, that AI was the ultimate application of high-performance computing.

And with that, the investment cycles would be there. I think this is way before ChatGPT and large language models, but it was the idea that we could really use computing to do so much more in terms of unlocking productivity and intelligence going forward. So yes, we've pivoted our -- really, our R&D capabilities, both hardware, software, system integration, to a significant focus on high-performance computing and AI.

I think it's paid off well. Our data center business has grown very nicely, well ahead of the market, over 50% a year for the last few years. And what we see going forward is even more exciting. Because I think the recognition is that computing is such an important part of the ecosystem today that we see a very large market opportunity as well as significant growth of our business, actually accelerating growth from our, let's call it, 50% plus over the last few years to over 60% plus as we go forward. So no question, data center is the place to be.

Yes, I actually wanted to ask you about that. So you had this Analyst Day recently. You gave us a new $1 trillion data center TAM by 2030. You were saying $500 billion by 2028 before, so you've upped that, but you also importantly said that you can get double-digit share of that pie. You're doing $16 billion in data center this year, would put you on a 60% CAGR, as you said, that's up from the 50% CAGR over the past 5 years. How are you winning? And what's the crux of your competitive advantage in data center?

Well, I mean, when you look at what's important in the data center market, I think the key piece is you really have to have a holistic view of the market. It is CPUs, it is GPUs, it is FPGAs, it's the possibility of doing ASICs, it's being able to integrate all of that together. And that's our unique capability. I think we are really the only semiconductor company out there that has all of this foundational IP, and we have invested way ahead of the curve in terms of some of the key enabling technologies. We were the first to implement chiplets in high-volume production. We're now on our fifth generation of chiplets.

And the reason I view these as key foundational technologies is because the one thing we know is that the workloads are going to change. There's nothing static about the computing market. There's nothing static about AI. What we see is that there's an incredible pace of innovation out there where there're new workloads, there're new models and there're new use cases. And so you really need this entire portfolio of technology capability which is what we have. So we've built an incredibly strong franchise with our EPYC data center server CPU chips. We're now over 40% revenue share in that market and growing.

We have a very, very strong GPU accelerator road map. And yes, we view that as a significant growth opportunity, the largest piece of the TAM.

I think, Tim, you might remember when we originally said that the TAM was $300 million or $400 million people thought, "Wow, Lisa, that's really big." And now I would say that I think we're all believers in the TAM is very, very large because we're still in the early stages of this. And our differentiation is going to be offering the right solution for the right workload going forward.

So there's been some recent news in the market that have made people think that ASICs are going to take over the accelerator market. And I just wanted to get your opinion on that and sort of the general competitive landscape in the AI world. Is our ASIC really a threat to GPUs? You've said that ASICs are going to be 20%, 25% share of the market. Has anything we've heard recently changed your view on that?

Yes. I actually don't think so. I think what we have said about the market is what I started with, which is, the market wants the right technology for the right workload. And that is a combination of CPUs, GPUs, ASICs and other devices. As we look at how these workloads evolve, we do see some cases where ASICs can be very valuable. I have to say that Google has done a great job with the TPU architecture over the last number of years. But it is a, let's call it, a more purpose-built architecture. It's not built with the same programmability, the same model flexibility, the same capabilities to do training and inference where that GPUs are. GPUs have the beauty that they are a highly parallel architecture, but they're also highly programmable. And so they really allow you to innovate at an extremely fast pace.

So when we look at the market, we've said that we see a place for all of these accelerators. But our view is, as we go forward, especially over the next, let's call it, 5 years or so, that we'll see GPUs still be the significant majority of the market because we are still so early in the cycle and because software developers actually want the flexibility to innovate on different algorithms.

And with that, you're not going to know our priority what to put in your ASIC. So I think that's a difference. So 20% to 25% feels like the right number. I think the other thing that people should recognize is that this is absolutely a huge and growing market. And as a result, you're going to see a lot of innovation on the silicon side as well as on the software side. And in general, I view that as a great thing because that allows a differentiation in the market.

And if a customer came to you and wanted you to build an ASIC for them, is that something that you would do?

Well, the way we look at these things, Tim, is it's all about what is our secret sauce, what is our differentiation? And from our perspective, the differentiation really comes when we can take our intellectual property together with our customers' intellectual property and know-how and create a case where 1 plus 1 is greater than 3. I think we are extremely good at deeply partnering with customers, and we've done that over the last 10-plus years. We do have -- in addition to all of our standard products with CPUs and GPUs and FPGAs, we've also created a semi-custom business. I don't call that an ASIC business and the differentiation being ASICs are, you're going to do, let's call it, any chip that somebody comes and asks you to do. That's not necessarily where we shine. I think where we shine is when we can put our IP together with our customers' IP. And we have done a number of semi-custom designs that build off of our foundational capability so that customers can differentiate.

So I think our overall value proposition is our goal is to take all of our R&D investments, and we now have 25,000 engineers that are integrating at the bleeding edge of technology, hardware, software, system design, and really marry it with our largest customers who want to find that differentiation and work on how do we see that in the portfolio, and that could be a custom system design. So we do, do sort of putting the pieces together, that could be a special SKU. We have lots of special SKUs that are optimized to given workloads. And that could be a special silicon as well. And we've done that in a number of cases across a number of markets over the last couple of years.

Great. So I wanted to go on to another debate that's in the marketplace, and that's whether there's a bubble right now in AI. You weren't going to get away without me asking you this.

Well, wasn't the first question so...

So can you just talk about that? I know NVIDIA went at that pretty hard on their call. So I just wanted to give you a chance to address that?

Yes, absolutely. So it's kind of curious this -- the conversation about a bubble from my standpoint. I mean, I spend most of my time talking to the largest customers, the largest AI users out there. And there's not a concept of a bubble. What there is a concept of is, we are, let's call it, 2 years into a 10-year super cycle. And that super cycle is computing allows you to unlock more and more levels of capability, more and more levels of intelligence. And that started with training being the primary use case, but that's really very quickly migrated to inference. And now we're seeing, with all of the models out there, there is no one killer model. There's actually a number of different models that are, let's call it, some are better in certain aspects, some are better in other aspects. Some people want to do, let's call it, fine-tuning, reinforcement learning. So with all of this capability out there, the one thing that is constant as we talk to customers is we need more compute. That at this point, if there was more compute installed, more compute capability, we would get to the answer faster.

And so yes, there is significant investment. I mean, I think all of the CapEx forecasts that have increased over the last 3 to 6 months have certainly shown that there is confidence that those investments are going to lead to better capabilities going forward. And so yes, from the standpoint of do we see a bubble, we don't see a bubble. What we do see is very well-capitalized companies, companies that have significant resources, using those resources at this point in time because it's such a special point in time in terms of AI learning and AI capabilities.

And I guess just on that end, so there's a lot of talk that there's not an ROI for these CapEx dollars. I know that people say that they're short on compute. But when you look at AI and the actual use cases, can you speak to that?

Yes, absolutely. I think, again, what my -- my view of this is the cause and effect usually takes a little bit more time than people are expecting. But what we're seeing, and I can just tell you our own case at AMD over the last 15 to 18 months. What started as, let's call it, let's try AI for our internal use cases, has now turned into significant clear productivity wins going forward. So there's no question that there is a return on investment for investment in AI.

What is the return on investment for enterprises? It is more productivity. It's building better products. It's being able to actually serve your customers in a way that is more intuitive than you have today. And if you look at today's AI, as much progress as we've made over the last couple of years, we're still not at the point where we're fully exploiting the potential of AI. So we're seeing actually a lot more effort over the last 3 to 6 months on the use of agents and how we make sure that AI not only suggests answers in a Copilot fashion, but actually gets to a place where it can actually do a lot of productive work. And that is flowing through. We're seeing that across multiple customers. We're seeing that across the largest hyperscale customers. We're seeing that across the large enterprises that are using AI.

And I still say that we are in the very, very early innings of seeing that payoff. So as we talk to the largest enterprise customers, I think every conversation is, "Lisa, how can you help us, how can we learn faster so that we can take advantage of the technology?" So I think the return on investment certainly will be there. I think the debate is perhaps more around the largest foundational model companies and whether there's return on investment there. But again, my view is that there's not going to be one best something or there're going to be multiple models that are best optimized for use cases. And the secret sauce is really in how you integrate it so that customers can take advantage of the technology as smoothly and as easily as possible.

So another point is that you're moving from being a silicon company to being a systems company. And a big piece of that was your acquisition of ZT. And then you -- and your partnership now with Sanmina. So can you actually speak to that? And you're a bit of a fast follower in building these racks in these system. So do you think that you've learned from some of the growing pains that your peer had?

Well, I think if you take a step back and come to why are we doing this integration, the reason we're doing this integration is the time to useful capability, sort of the time that it takes for our customers to bring up this really complex infrastructure is super critical to make as fast as possible. So the full stack solution is a way for us to help customers get to, let's call it, productive compute capacity. And we're very happy with our acquisition of ZT. I think it's one of the smoothest acquisitions, integrations that I've seen. And what we've been able to do is really take, let's call it, best-in-class system design and combine it with our best-in-class hardware and software capability to come up with very, very strong full stack solution. We're super excited about MI450 series and the Helios product that will come to market in 2026.

I do think we have learned. I think we learned as a industry, we're always going to learn that putting together these complex rack level systems is hard. There's nothing new about it, but there's certainly ways that you can derisk and ensure that you can go as fast as possible. I think key elements for us in our strategy when we think about our [ global ] solutions is as important as it is to have that reference design capability, it's also really important to have an open ecosystem. And that open ecosystem means that we have an open rack architecture, which, together, we've developed with Meta, which I think has taken a lot of the best practices out there in the industry.

We're working with all of the key suppliers within the rack to ensure that, again, that we learn how to bring these up as fast as possible. And then frankly, the ZT team has brought 1,000 plus really skilled engineers to the capabilities. So I think we feel really good about our rack level solutions. I think the feedback that we've been getting on the Helios rack has been fantastic. I think people see that we've made really smart engineering decisions to ensure that we're able to bring these systems up as smoothly as possible.

Great. One thing I also hear is that you're fighting a battle on multiple fronts. You're fighting Intel and AMD in PC, you're fighting NVIDIA and you're fighting ASICs, and you're not that large of a company yet. So when you think about prioritizing development, do you feel like you're having to sort of disinvest in certain areas and invest in others?

Well, actually, I think you're actually pointing out one of our strengths. So I think one of our strengths is the fact that we have a really, really capable and efficient R&D engine. I give Mark Papermaster and the team a lot of credit for that. We've built an execution engine. We've done 5 generations of server CPUs right on time, app performance best-in-class. And the way we develop is we actually develop foundational capabilities that bring all of these computing elements together, so CPUs, GPUs, FPGAs. I actually think this is 1 of our strengths.

We're not religious about like the world is going to be taken over by X because I can tell you for sure, I do not believe the world is going to be taken over by X. I think you're going to need the right compute for the right workload, and that is our strength. And I think we've developed an R&D engine that knows how to execute that. Now there's no question that AI sits above all of this. And so all of the innovation that we're doing in AI, all of the software investments that we're making in AI are there to ensure that it works across the entire portfolio.

Great. Can we talk about the deal with OpenAI. You offered them 10% of the company with warrants. There's various strike prices at each tranche. How did the deal come together? And how does it change your engagement with the other customers?

Well, first of all, we're very pleased, excited, happy with the OpenAI deal and partnership. To give you some idea of how it came together, it really came together over the last couple of years. We've always been working with them as one of the leading foundational model companies to understand where do they think model evolution was going because that's so critical in determining sort of our long-term road map. When we were looking at what should the MI400 series look like, what would really make it special, how do we differentiate long term? Clearly, one of our key strengths has been our memory architecture that's enabled by chiplets and all that. And a lot of that came from talking to our largest customers, OpenAI being one, but a number of our other large partners, Microsoft, Meta, Oracle, et cetera, also contributed to those thoughts.

And when we thought about sort of where you want to go going forward, this is all about going big and not necessarily the typical way that technology evolves is sometimes, "Hey, we do smaller partnerships here and there." In AI, it's all about really bringing together hardware, software, cooptimization and codesign. And that's what we've really put together with this OpenAI partnership.

I think we view it as a way to ensure that we are highly developing with one of the largest model companies in the world. The key here is that with the current structure of our 6-gigawatt partnership, it's a win-win on both sides. So on one hand, we get significant scale with this. If you think about it as each gigawatt is deployed, that significant scale to AMD, that's double-digit billion dollars of revenue. And it's also an opportunity for OpenAI to be very invested in our technology success as well because there are a number of commercial as well as technology milestones. Very much a win-win, very highly accretive to our portfolio.

And as it relates to other customers, I think the idea of having a very optimized road map is a good thing, and we view it as -- again, there's -- as much as we love OpenAI, we also deal with the entire set of customers out there from an AI-native standpoint as well as the largest hyperscalers, and we're seeing great traction with the road map.

And are you any more engaged? Have you had any more conversations lately that you might not have had, had you not announced that deal?

I believe that it has given people a view of sort of AMD's capabilities. I think we always had good conversations, but I think the idea of just how competitive the MI400 series road map is, what we have going forward has certainly been helped since we announced the OpenAI deal.

And do you worry about customer concentration? Can you speak a little bit about breadth? If you look out in your forecast, how broad will your customer base be?

Yes. Look, our view is, we are a general purpose supplier in the sense that OpenAI is a great partner, and we very, very much believe in their success and their road map. But we are highly engaged across all of the largest hyperscalers out there. And from a customer concentration standpoint, the key point is this is a big multigenerational, multi-gigawatt partnership. We have a number of others that are at similar scale, similarly multi-generation. And the truth is compute is a premium. Like this is one of the areas where there are so few companies that can offer this capability. I'd like to believe that in addition to great technology, we focus on our customer success. So it's about total cost of ownership, ensuring that there's significant differentiation and also ensuring that we're very flexible in how people want to operate in terms of the overall ecosystem.

So from that standpoint, I don't worry about customer concentration. I view this similarly when -- if I give you the example of where we were in the server CPU market when we started with the hyperscale accounts, they didn't all start on day 1 at the same time. They -- different hyperscalers went large at different points in time. And that's the same thing that we're going to see in the AI accelerator road map. We're seeing a very similar pattern in terms of how we engage with customers and how customers view AMD as really a long-term partner, especially since there's this recognition that, in addition to the GPU road map, the CPU road map, the networking road map, the overall sort of capabilities are very attractive.

Great. Well, we've made it to 23 minutes, and we haven't talked about CPU yet. So maybe we can talk about that. So demand is obviously very strong in both PC and in server. So maybe we can talk about that. We keep hearing about hyperscalers asking for supply, and we keep hearing about long-term contracts, particularly on the server side. So can you just talk about that and just talk about the supply environment?

Yes, absolutely. The last, I would say, several months has been a very interesting story around the CPU world. We are really happy and proud of our partnerships on the CPU side. I think there was this narrative last year that somehow GPUs were going to take over the world and refresh cycles for CPUs would lengthen and you wouldn't have as much, let's call it, market momentum. I think what we started seeing at the beginning of this year is actually a significant refresh cycle starting. So that was very positive. But more interesting is, over the last 3 months, what we've seen is really a significant uptick in CPU demand.

And when you look underneath that, it's not just refresh cycles. I mean, there's no question that there were some refresh cycles that were, let's call it, delayed as a result of some of the AI CapEx spending. But a lot of that is being caught up now. And what we're also seeing is that as AI moves to more inferencing and there's more work being done and things like the agent workloads are starting that they're spawning more general purpose CPU needs. Because if you think about it, if you have, let's call it, 1,000 agents or 1,000 virtual employees, they need to operate on some data set. They need to operate on some computing capability. And that requires general purpose CPUs.

So we actually have a view that the CPU market actually will substantially grow over the next 4 or 5 years as we see the AI usage really spawn more traditional computing applications. So it is certainly a good thing to see. We love seeing that. I think it's one of the reasons that we're so passionate about the overall road map being important in terms of all of the capabilities. And we see the CPU business has a great business going forward.

And you've gained a bunch of share in data center. Do you think that in server, has your lead at all shrunk? Do you think that you'll continue to gain share?

We do. We're in a very fortunate place right now where we are a trusted partner on the CPU side, especially for the largest hyperscalers. And the conversations are such like how can we work together to build, let's call it, the best-in-class road map going forward. I think as great as our fifth generation Turin is, we're super excited about our next-generation Venice CPUs. We think that extends our leadership going forward, and that extends as we go into the next generation as well. So I think we have a very strong franchise there. And the key is we're a trusted partner going forward.

We're also quite underrepresented in the enterprise space, but I've seen that also as a significant growth opportunity for us. The largest enterprises are all looking for help as to how they modernize their data centers and how they make their choices, and we're very happy to be part of that conversation.

Great. One thing that I was quite surprised about from the Analyst Day was that you had pretty strong share aspiration gains in client actually. You think you can be more than 40% share in the client. Can you just talk about that?

Yes. So the client PC business has been a place for us that it's not a market that is necessarily growing by leaps and bounds, but it is a important market. It is a market that has very good customer-facing capability for us. And we've grown extremely well over the last couple of years. I think we've really streamlined our road map. I think we have put it as an AI-first road map, and that has been appreciated. We're now, let's call it, mid- to high 20s share. And as we go forward, we see that only growing.

There are areas where I think we are already best-in-class when you look at things like the desktop gaming market. This is an area where we've had historically a lot of success, desktop channel market going forward. And we're continuing to grow sort of in premium notebooks, let's call it, the most valuable part of the PC TAM is where the product does matter in the premium segments, and that's where we're actually gaining the most share because our products are superior.

Do you worry that -- because memory prices have gone up so much, do you worry about some despecking in PC? Or do you worry that it hurts the market at all, that it hurts demand?

Yes. I mean we're certainly watching, Tim, the commodities. There's no question that as the market has gotten tighter, some of the commodities like memory have become tighter. And we certainly are watching for that. I don't think it's a major perturbance to the market. I think it might be a minor perturbance, and we're watching that closely.

Great. And maybe we can talk about some of the bottlenecks that you're worried about over that 2030 forecast you gave. Are there things that you're worried about, like HBM or CoWoS? Or what is something that kind of keeps you up at night that could constrain your growth?

Well, the great thing about the semiconductor market is, I think we are used to expanding and expanding quickly. So if you put aside sort of very temporal things, what are the most important things? It is advanced technology, sort of access to the most advanced wafers. It's high-bandwidth memory, it's packaging CoWoS, these elements. We have built a very, very strong supply chain over the last couple of years. We have deep partnerships with TSMC, all of the memory vendors, all of the packaging vendors. And I think we feel very confident that we can achieve our growth rates.

I think the industry as a whole is very much around ensuring that we do satisfy all of the demand that's out there. The other area that we're watching very closely is power and how data center power is coming online, not just in the United States, but across the world. I will say that this administration has really activated a lot of the power build-out. So we're seeing things moving faster. We're seeing that there is a desire to put more power on as quickly as possible, trying to get rid of some of the bureaucracy around that. And I think those are all good things.

We're also looking at power outside of the United States. And so there are lots of opportunities. We didn't get to talk about sovereign AI and a lot of the nation state investments that are happening there, which we think are another adder on top of it.

So I would summarize it, Tim, as it is -- there're lots and lots of things on the radar screen, but the most important thing is that everyone in the ecosystem recognizes how important the enablement of this computing technology is. And so we're all working together to do that.

Great. Well, we're out of time. Thank you, again, Lisa.

Wonderful. Thank you so much.

Welcome to AMD Financial Analyst Day. Please welcome to the stage, Matt Ramsay, CVP, Financial Strategy and Investor Relations.

Good afternoon, everybody. Thank you for enduring snow and cold weather and anyway, Happy Veterans Day, even though you guys had to endure the parade, but it's an important thing for us to acknowledge. It's been a very interesting 12 months or so since I joined AMD. We've been 2.5 years or so since we've done an Analyst Day. And thank you guys all for coming out and spending some time with us. I know your time is valuable, and we really appreciate it.

I get to present the most exciting slide of the day. For you with really good vision, this is our cautionary statement. It turns out the entire point of us having one of these analyst days is to make forward-looking statements. So we're going to make plenty of those. But it is important that you guys take note of the cautionary statement and go look for the risks and uncertainties that come with the forward-looking statements that we make. Those are going to be in the 10-Q and 10-K filings that we file with the SEC, and you guys know the drill.

In addition to that, pretty much every number that we're going to talk about today on stage is a non-GAAP number. In the appendices of the slides, and in our SEC filings, there are GAAP to non-GAAP reconciliations of all of those numbers. So please take note of that as you digest the financial information that we give here today.

So here is the agenda. I think we have a lot of really substantive things to say that are relevant to the market, relevant to our business, relevant to our technologies. You guys will get to hear from Lisa, introducing everything about AMD and from Mark to talk about the technical road maps over the last 10 years and over the 10 years coming in the future.

Then Forrest and Dan and Vamsi are going to spend about an hour going through the data center business in detail. And then we'll take a break. The one thing that I would ask you, we're webcasting all this and trying to time it and everything, if you could please take your 20-minute break and be back in your seats when we get ready to kick off again, I would really appreciate it.

After the break, Jack, and Salil will talk through both the Client and Gaming business and the Embedded business at AMD. And then Jean will come up with the -- I don't know, the financial model that you guys have sure will stick around for at the end, and then Lisa will bring us back to close before we do Q&A at the end. It should be a really, really good day, and we're very thankful that you've come and spend it with us.

All right. So with -- now without further ado, I get to do something that's an absolute thrill for me personally, but probably unnecessary for you guys, introduce someone that absolutely needs no introduction to this audience, but there's our Chair and CEO, Dr. Lisa Su.

All right. Good afternoon, everyone. Thank you for joining us here in New York, and thanks to everyone joining us online. We're really excited to be here today, excited to see so many friendly faces and really excited to talk about really what's happening in the market. I mean it's a really special time for the technology industry, for the technology market, AI overall. But then also, it's a very exciting time for AMD.

So let me go ahead and get started. What I'd like to do is basically give you a bit of an overview, starting with some of our strategies, some of our execution and then really what we see going forward and then have the team really take you through the deep dive. But starting first with our overall mission. I think we've been very consistent here that high-performance computing is the foundation of really everything that's important.

When you talk about solving some of the world's most important challenges, we've actually added its high performance and AI computing because AI is such a large piece of the computing world today. But this is our goal in life. This is what we wake up every day to do. And when we look at where we are today, I'd like to say that AMD computing touches billions of people every day because it's true.

When you look across data center, cloud, some of the most important services that everyone uses in enterprises or personal, talk about edge and intelligent devices, talking about all of the different market segments where you need and use computing. You can see AMD throughout all of those places. And increasingly, you see AI everywhere as well. So that's going to be another theme of our day is that, yes, we're going to spend a lot of time talking about the Cloud and the Data Center because those are the largest markets, but we're really seeing AI diffuse through all aspects of the business, including it at the edge and in the client world.

So a little bit about just focus areas and how we got here. When I look back, you say so much has changed over the last few years, if you think about how the market has really evolved over the past few years. But actually, for us, our strategy has stayed very consistent. I mean this is what we said were our strategic pillars a few years ago. And when I look back on it, I say these -- actually, these are exactly the things that we invested in, exactly the things that we were focused on. And I'll talk a little bit about how things have evolved over the last few years.

Certainly, compute technology leadership, that's our foundation. We have to have the compute capability. But we said that data center was going to be the most strategic market for AMD. This is where we saw tremendous growth. This is actually the part of the industry that fits our capabilities the best. And we've really expanded our data center business. Ensuring that AI goes across every aspect of our product portfolio was also a key pillar and ensuring that we had the software. Many of you guys have asked us about our software and what we're doing around the software. So we'll talk more about that.

And then also using the entire power of our IP portfolio to offer both standard as well as semi-custom solutions. And these are areas where we think over the long term, there are lots and lots of needs for compute in the industry, and you want to have sort of the right compute for the right workload. And so you need sort of the whole tool chest to really go through that.

And so just taking a look at some of the progress over the past couple of years, I think our data center execution has been extremely strong. I mean we've grown this business from sort of $2 billion in 2020 to this year, estimated over $16 billion, over 50% growth rate. And when you look at sort of the underpinnings of that, I think there's no question that when we started with the Zen road map family and EPYC, I think people were wondering, hey, just how much traction are you going to get? Lisa, is this a place where you have durable long-term competitive advantage?

And I think we've taken what was a very, very clear technology bet and really understood where the market is going. And as a result, you see EPYC now in just about everywhere. So 10 out of the 10 largest hyperscalers, every one of the most important social media services -- we're actually in over 60% of the Fortune 100 today and growing. And so that's been a really strong success story.

And then we've also had our instinct and our GPU product line, which with MI300, MI325 and now the MI350 series, we've ramped this faster than any other product in AMD history with 7 out of the top 10 AI companies now using AMD Instinct products, the top 2 fastest supercomputers and many strategic partnerships that have been announced and are in the works.

So looking at some of the numbers, what does that meant to our business? We are now, let's call it, roughly about 40% revenue share of the market in the most attractive segments of the market. So large hyperscaler, very much standardizing AMD technology through our 5 generations in addition to growing enterprise footprint. What that's meant is data center is now our largest segment, so almost half of our business, 47% of our business to be approximate there. And it's growing. It's growing much faster than the other pieces, one, because the TAM is growing and the other because we are extremely well positioned from a product portfolio standpoint.

On the AI side, we're going to talk a lot about AI today. We're going to talk a lot about data center AI. But I just wanted to put the picture in terms of what's been happening in the AI business for AMD. Clearly, the rate and pace of innovation in AI is faster than anything that we've seen before. We've compressed our schedules so that we went from what was a 2-year cycle to an annual cadence. I think we've done well with adoption of MI300, MI325 and MI355. But there is a huge step function in front of us with the MI450 series. And that's both in terms of technology, just sheer raw capacity capability as well as going to our rack-scale solutions as well as, frankly, where the market is.

So it's a confluence of sort of technology at the right time for a market that is inflecting in a big way as we're going forward. Just a little bit of an overview on the Client and Gaming business. You're going to meet Jack Huynh today. Some of you may not have met him before. He's our new leader in the Client and Gaming segment. I think we've done a fantastic job with this business. People have often asked us, what do we think of the PC market? I mean the PC market is a great market. It's a market where a lot of silicon is consumed. People -- this is how people really touch compute. What was most important for us in this market is that we pick the segments where technology mattered and we focused on those segments and ensured that we had product leadership.

So when you think about premium, when you think about gaming, when you think about enterprise, those are the most attractive portions of the client market. And that's where we have done exceptionally well, particularly over the last 12 to 18 months. And so when you put all that together, we are currently at 28% client revenue share, but we're really inflecting in this business as well, over 50% of the desktop CPU channel. And we're at a place where AI is really in its infancy in the PC market. And so as that scales, I think our product portfolio strengthens, and we see this as a growth market for us as well.

And then you're going to hear from Salil Raje on the Embedded business. And the Embedded business is really primarily the former Xilinx's business together with the embedded x86 business. And what you see in this business is, put aside, there's been some, let's call it, noise over the past couple of years as there's been a bit of inventory correction overall. What we see is an extremely strong business that has access to a set of markets that AMD never had access to before.

So over 7,000 customers, automotive, industrial, aerospace and defense, communications, we are in all of the largest firms from a technology standpoint. And perhaps in this business, the most important metric is not near-term revenue because the near-term revenue is very much a matter of things that happened in the past. But the most important thing is design win growth. And what you're going to hear from Salil is since the acquisition of Xilinx and the integration into AMD, we have grown design wins at a significantly faster pace than sort of the former Xilinx from an FPGA-only portfolio.

Our design wins have exceeded $14 billion in 2024. We're on a path to exceed $16 billion this year. So over $50 billion of new design wins at a CAGR of 21%. And that gives us great confidence of the long-term revenue trajectory and Salil will talk about some of the ways where bringing the portfolio together has really ended up with tremendous synergies.

And then one area that we haven't talked a lot about with this audience, but I thought this would be a good time to bring out is where we are with semi-custom design wins. If you think about my philosophy on semi-custom has always been you need to have the right product for the right workload for customers. And when you get into very high volume or very specific workloads, there are advantages to taking a standard product and really customizing it for those specific workloads.

What has been well known for AMD is we've been #1 in game consoles for a long time. We're very thankful of the relationships that we have with the gaming customers. And that was, let's call it, about $20 billion of lifetime revenue, give or take, when you think about a 5- or 7-year cycle of that business. What we have done, though, over the last 12 to 18 months is really expand the aperture of what we do in terms of semi-custom. And what that has really done is it's taken -- certainly, we're very pleased with this traditional Gaming business that we've been a part of. But we've closed new design wins in Aerospace and Defense, Automotive, Data Center as well as communications, now totaling over $45 billion of one design win content that would be revenue starting ramp in 2026 and beyond.

So I think that tells you the power of the model. We'll talk a little bit about the technology underlying that and how we've been able to do that with some of the technology investments that we've made. But this is an example of where I think our strategy, our long-term strategy in investing in a modular IP road map and the ability to mix and match the different pieces of our IP portfolio has really paid off. And I think this will be an area of consistent growth for us going forward.

So putting all that together, the other piece of it that I think we have been very focused on is strategic investments. This is an area where we always have to bet ahead of the curve. We have to make strategic bets. And there's no question, by and far, the largest bet that we've made is really in AI. So if you look at just our organic investments, we've significantly ramped the engineering talent in the company. So we have over 25,000 engineers now, both hardware and software. The largest growth in engineers has been in software and platform, which you might expect given where we're going with AI systems.

We've also built what I would say is an M&A machine. We are now very confident and comfortable with M&A execution. I know that when we first started this, Xilinx was the first large acquisition that we did. People were always like, Lisa, are you sure you can integrate this and add value and not have dis-synergies. I think what we've shown is excellent execution on the large ones. Xilinx has added in addition to the revenue in the business that we're going to talk about, it's added significant technology capability when you think about the AI engines, when you think about the SerDes capability, when you think about the software capability and our leadership of our AI is under Vamsi Boppana right now, who is a very key executive in that.

We also have added with Pensando, significant networking expertise that is a key part of our AI capability. And with ZT Systems, we've added all of the rack scale solutions expertise that has, from day 1, started adding value and accruing value to our MI450 and Helios capabilities. In addition to that, we've done a number of software acquisitions. Many of these are smaller acquisitions. But what we've done here is really in the essence of trying to ensure that we have as much and as fast a ramp as possible on our ROCm software stack. We have found some excellent, excellent teams that have added to Vamsi's team to ensure that we have best-in-class from a software standpoint.

The other arm that we have started with is a much more active venture investments arm. So I would say this was something that we didn't do as much a few years ago, but now this is an active part of our portfolio. Matt, Huynh runs it for us. And here, what we're looking for is really -- I wouldn't say picking winners and losers because it's hard to pick winners and losers when you're investing in start-ups, but it's picking leaders who we think will be influential in the AI ecosystem, whether it's hardware, software, application level, model level to ensure that we are keeping a good pulse on what's happening in the ecosystem and also ensuring that we familiarize people with AMD content.

So these are the key strategic investments that we've made, which I think have really added to our capabilities in AI. So putting that all together, I think the overall revenue growth story has been a strong story. We're now projecting to be about $34 billion this year from a much smaller number in previous years. And that's also significantly improved our overall profitability. So that kind of gives you an idea of how we got to today.

Now let's talk about where we're going because I think that is -- clearly, there's so much that's going on in the industry. We want to give you a sense for where we think our place in the industry is and also where the market is really going.

So starting first with some of the market trends. I'll spend a little bit of time in terms of the market trends because I know that you guys are all thinking about these things. I would say that the rate and pace of change in AI is certainly beyond anything that I've seen in my sort of tech history. Super intense speed and pace that you're seeing just massive infrastructure build-out.

And what do I mean by that? I mean when we talk to all of our largest customers, we've actually seen this because we're always talking to our customers. And if I ask them last year, they might have said, "Hey, Lisa, we're investing right now, but we believe it will level off as we -- as the compute comes on board. And if you ask them today, what they would say is, no, it's actually not going to level off. We need to accelerate. We need to put more AI infrastructure in place because there's a real belief that AI compute really equates to intelligence.

So if you have the chance, if you have the balance sheet, if you have the capability to put on more compute, you're going to do it because it's going to give you incremental advantage versus your competition. So it's really, I would say, an insatiable demand for AI compute, which is kind of interesting. There's obviously lots of work on power and ensuring that you get the full power and data center build-out ready for that.

We're also seeing that it is really too early to talk about the winners and losers. There are lots and lots of models out there. I think the number of models is growing. In addition to the foundational model companies, which most people spend time on, there are lots of the next layer of companies that are helping enterprises really use AI in terms of fine-tuning and other services that you add to it. And I think that's going to become more important.

And then probably one of the trends that you guys have asked us about from time to time is what happens to the CPU market when you have AI accelerators growing so fast. And I would say, up until this year, there was a little bit of a thesis that somehow GPUs would take a lot of the CPU workloads. I think what we have seen is that is not true. We've actually seen the opposite be true. As we've gone through -- as more AI has deployed from a -- let's call it, the models are out there as people are using AI in enterprises, we're actually seeing that the CPU demand is accelerating.

And Dan McNamara is going to talk a lot about why we think that's happening, what we see. And it's now a consistent trend that we're seeing with multiple large customers as well as both on the cloud side as well as the enterprise side. So I think it's a great thing. I think it makes sense, right? AI is driving new intelligence, new capability. As you have more agents out there, those agents have to work on something, and they're working on things that require more general purpose compute.

We are seeing inferencing growing faster than training. I think that's all -- that's something that we all kind of expected but we're starting to see that now. The enterprise class solutions are also starting, I would still say, relatively early in the adoption curve, but there's a lot of interest and experimentation there. And then clearly, sovereign AI and nation scale deployments is an area where we continue to see every country wants to have control of their own AI compute, and they want to be able to select how they deploy that. And this is one of the key trends that's also accelerating the TAM growth overall.

And then as you go beyond the cloud, I think we will see more AI, whether you're talking about at the edge or in PCs as this is a way where new experiences are coming in. This is also still early in the cycle. So I would say this would be in this -- in the latter part of the growth for our long-range plan, but it is certainly an area that we think is interesting.

So when you put all that together, and we talk about what market sizes are, it's always hard to call what a TAM is. When we first started talking about the AI TAM, I think we started at $300 billion, and then we updated it to $400 billion and then $500 billion. And I think many of you said, well, that seems too high, Lisa. Why would you think that those numbers should be so high? It turns out that we were probably closer to right than wrong in terms of just the acceleration of AI spend. And that really came from, I would say, lots of discussion with lots of customers in terms of how they saw their computing needs frame out.

So when we look at the market now, what we decided to do is a couple of things. One is extend our range from -- really over the next 5 years, so 2030 TAM and also think about it as a more inclusive TAM to not only include accelerators, which is very important, but also include CPUs and some of the networking content that we add to. So our current perspective is we're talking about a TAM that is greater than $1 trillion by 2030. We've seen -- if I look at our previous forecast at greater than $500 billion for 2028, we're seeing that number come up significantly. And then we expect that there will be extensions of that growth rate going into '29 and '30.

So it's an exciting market. There's no question. Data center is the largest growth opportunity out there and one that AMD is very, very well positioned for. So that kind of sets the stage of where we're choosing to invest going forward.

So now in terms of our strategic pillars, similar types of things. I mean what I really want to emphasize is our strategy has been very, very consistent, and I think you need to be when you're in the technology space because, frankly, these product cycles are long. These are our strategic pillars, and this is how we're going to organize the day for you. We start with just compute technology leadership. That's foundational to everything that we do. We are extremely focused on data center leadership, and that data center leadership includes silicon, it includes software. It includes the rack scale solutions that go along with that.

And then part of our software story is not just that we will have a very competitive software stack. It's also that we have a software stack that extends across all platforms from cloud to edge to client. And so we'll talk a little bit about that and then powering AI everywhere outside of the cloud. So if I just double-click on each one of those to kind of give you the highlights of perhaps what we would like you to spend some time on.

First, on the technology leadership. Mark Papermaster is going to take you through this. Mark's been my partner over the last 10 years, really developing sort of the entire technology stack that we have at AMD. I think we're very proud of the fact that we made some good decisions along the way.

Clearly, our investments in the foundational CPU and GPU IP were critical. But that's really expanded. I think the fact of the matter is these systems are getting so complex these days. I mean our chiplets now are mixing and matching all of the latest and greatest technologies. It's extremely important to have the right interconnect. I think our bet on Infinity Fabric allowed us to do that. I think we're leading the industry in terms of advanced packaging, and we're making significant investments in the SerDes and other technologies that are necessary to interconnect all of these things. So Mark will take you through that.

But the key is our absolute commitment is ensuring that we invest for technology leadership across the Board. And I think the team has done a great job with that.

In terms of the data center, as Matt said, we're going to spend a good amount of today on this, about an hour, and that's going to be gone through with Forrest, Vamsi and Dan. This is really an all company affair. So if you want to talk about something that is front and center for everyone at AMD, it's ensuring that we accelerate our data center leadership.

I think the thought here is really across all of the major segments, Cloud, Enterprise and Supercomputing, ensuring that we have the right compute for the right workload. I am a big believer in there is no one solution that scales across every market segment. Every segment has kind of a uniqueness that it's trying to drive with its workloads, and that's across CPUs, and GPUs and accelerators as well as across sort of high end, midrange and sort of entry level. And so I think we've built an expansive portfolio to do that, and we'll talk more about that.

I think the other piece of our data center strategy that is unique is our intention is to have an extremely capable full rack scale solution, but it's also to do that in an open ecosystem. And in an open ecosystem means that our customers can decide whether they use all AMD, which we're very happy if they choose to use all AMD. We're also okay if they want to interoperate with other standards out there because it's good for their business because they have other things that they're trying to optimize. And so that continues to be a key foundation and for us, we'll certainly go through that.

On the software side, I think we've made tremendous progress in this area. This has been one of the areas where people have said that we have needed to make significant investments, and I think we've done that. I think ROCm is today the industry's premier open source AI stack. What we've done is really get significantly more focused on ensuring that we have native support for all of the day 0 models, native support for all of the leading open source frameworks out there. And that has really been something that Vamsi and his team have worked on and also ensuring that we have the full stack of enterprise solutions and developer solutions out there.

I will say that we have greatly expanded from just serving the largest hyperscalers to really serving the broad developer base with ROCm. So this is an area that Vamsi will go through in quite some detail. And what that says is we have now all of the pieces to deliver full AI factories, and that is really our goal throughout this entire stack across CPUs, GPUs, software, networking and our cluster level systems design.

So in terms of powering AI everywhere, this will be covered by Salil and Jack. AI PCs, gaming, edge AI, physical AI, these are all places where we think our IP also plays very well and we will continue to ensure that we have a full ecosystem that covers all of these capabilities. So that gives you a little bit on the technology strategy.

In terms of what this means for our business. So I'm going to let Jean go through the full financial model this afternoon. So you have to kind of give us a few hours to go through all the technology. That's okay, right? I mean like we want to make sure that you stay interested and engaged.

But what I thought I would do is maybe give you a few nuggets of how we think about growth. So you can have that in mind as we're going through each of the presentations. So first, in terms of strategic partnerships. I think what has been really critical to us is to ensure that for -- to be successful in the AI industry, I think deep partnerships are super important because these systems are so complex and because these road maps are multigenerational. And I think that's really what we've done with our strategic partnerships.

We've recently announced several partnerships. I'd like to give you maybe a little bit of context of how those fit in and then how those fit into what we think our growth trajectory is. First, OpenAI, I think we're very excited to announce a 6-gigawatt partnership with OpenAI. They are clearly the leader in frontier model development. We have been working with them for some time, I would say, at the early stages, but the key is, I think we actually took the opportunity to develop MI450 series for many of the use cases that were most important to OpenAI, including optimized inference use cases.

And so to be able to announce a 6-gigawatt deal over 5 years, I think that sets a real foundation for our road map and for our overall adoption curve. We also announced an expansion of our Oracle partnership to MI450. So Oracle will be one of the first, if not the first, to offer public instances of MI450. And what we're really trying to do here is our experience is when we finish development to when you actually see a public instance can actually take some time because these are very, very complex systems.

Our goal really with these partnerships is to get it to the place where when we finish development, you can actually go and get an MI450 instance in the cloud very shortly. And so that partnership is really targeting third quarter and '26 as the ramp of that capability.

We also talked about and showed for the first time our Helios Rack scale solution with our partnership with Meta. And I'll mention a little bit of context of why this has been really critical. So first of all, Meta has been an excellent partner for us across MI300, MI350. They've been very involved in the software work, both on the PyTorch ecosystem as well as just the overall ROCm ecosystem. When we really went to rack scale solutions, we had 2 choices. Our choice was either we develop it on our own and have all kinds of proprietary system design, and we certainly could have done that.

But what -- in our true partnership mode, we said what the industry really wants is more of an open rack architecture because the idea that, yes, you can use AMD MI450s is great, but being able to ensure that there are set of open standards so that the ecosystem can develop more holistically was important. And so I think the Helios design based on the open standards with Meta really enables that. And so I think we're very pleased with that partnership.

And then we also announced a couple of new supercomputers with the Department of Energy that are adopting the MI400 series capabilities is actually a new product that we put on the road map, the MI430, which Vamsi will talk a little bit more about. So putting all of these in place, I would say, is a good foundation to talk about what we think our AI growth models are. What's been clear is that there's a lot of excitement around MI450 Series and Helios.

In addition to some of the customer engagements that we just talked about, we have strong momentum with multiple additional gigawatt scale opportunities. And those are very, very much in deep design phase with these customers. And I think what customers are telling us is, look, there's a real need for more compute. And what we have with the MI450 and with Helios is extremely competitive. In many cases, leadership when you think about the memory capability as well as the scale-up bandwidth. And we've also co-developed with these. And so we have multiple hyperscaler AI natives and sovereign opportunities that are well underway. And we talked about a revenue target of, let's call it, tens of billions of dollars in 2027. So I can say that we're on track to that.

But we also see a very clear path to double-digit share in this very important data center AI market. And that translates into, let's call it, a growth rate of over 80% AI revenue CAGR over the next 3 to 5 years. So when we talk about financial model today, we're going to frame it over the next 3 to 5 years. And so this is kind of what we see as our potential given the customer traction, both with the announced customers as well as customers that are currently working very closely with us.

Now as exciting as the data center AI revenue opportunity is for us, the other message that we want to leave you with today is every other part of our business is firing on all cylinders. And that's actually a very nice place to be. Some of the other key targets that we're going to talk about today are areas where we think we have the ability to grow significantly ahead of the market dynamics. So on the server side, Dan McNamara is going to show you sitting currently at approximately 40% share. We have a clear path to over 50% revenue share of the server market over a larger TAM, over the next 3 to 5 years.

Jack will talk about our client revenue market share. We're sitting, let's call it, in the high 20s right now. Again, a clear path to over 40% client revenue market share in this 3- to 5-year time frame. And then Salil will talk about what we see in the embedded market. Again, the opportunity to grow significantly ahead of the market to over 70% of the embedded adaptive revenue market share over the next 3 to 5 years.

So I think all of these are really exciting targets. I think each of the businesses will really go through their strategy, their product road maps and why they see a clear line of sight to this. At the AMD level, I want to give you some ideas of how we think about revenue growth at the AMD level. So at the AMD level, what we would say is we expect to really inflect in overall revenue growth over the next 3 to 5 years. We've done very well over the last few years, growing over 20%, but we really see the opportunity to now take the overall company at a scale of, let's call it, a baseline in 2025 of $34 billion to grow at over 35% CAGR at the total company level. And that's broken up into the data center business growing well ahead of the market at over 60% CAGR. And the core businesses, that's client and embedded, growing also well ahead of the market at over 10% CAGR.

So that kind of gives you a flavor of the type of growth that we're talking about. It's an exciting growth trajectory that we have. I'm going to leave the rest of the financial model to Jean, so she'll have a bunch to talk about as we wrap up the day towards the end. But that just gives you kind of a flavor of why we're so excited about this time in the industry and this time for AMD. I think we have just all the confluence of events that you would want to have if you're in tech today, right? We have a large and growing TAM, over $1 trillion TAM at the data center level, and that adds at the company level. I think we have a fantastic product portfolio.

I think as a team, I think hopefully, you will have seen that we are good at execution. When we say that we're going to do something, we're going to do it. We have a great foundation with deep strategic partnerships and the data center has tremendously expanded, and we have expanded our capabilities as well. So with that, let me turn it over to the team to give you all the details.

So Mark, do you want to join me, please?

Thank you, Lisa. And I really appreciate you all being here with today and the opportunity I have to share with you our technology portfolio. I've been in the industry 4 decades, and I've never seen such a time of just incredible pace of transition of new technologies and frankly, technology disruption. But guess what? We're a technology company at our heart. Myself, the engineers, the technologists in the company, this is the DNA that we have. This is what we were built for to take on these challenges.

So let's start today with taking stock of the AMD portfolio. Through focused R&D and inorganic acquisition, we've amassed the broadest portfolio of computation in the industry. And it's the computation that's required for the AI era. We've invested deeply across our leadership core IPs, our CPU with our Zen family of x86, our GPUs with cDNA for the data center with RDNA and gaming and edge applications. We have optimized for low-power inferencing with AI engines and our neural processing units. And we've extended our reach into networking with Pensando DPUs and of course, with the acquisition of Xilinx, a deep FPGA and adaptive compute portfolio.

What ties it all together? Well, that is our common foundational IP, a huge investment to make sure that we can seamlessly deploy these IPs across our portfolio. Our Infinity Fabric, providing that glue across all of the building blocks. Our system on a chip capability honed over years of development of leading-edge integration, physical design, verification, the kind of skills that you amass to optimize chip designs and then the leadership of chiplet technologies, partitioning into chiplets and leadership packaging capabilities. These have enabled us to quickly adapt to our customer requirements to integrate these solutions. And on top of that, we have software that ties this all together for AI applications, foundational across our portfolio with the ROCm stack.

And also very, very important and even more important in this AI era is security, and we have security building blocks, consistent security building blocks that we put in across our portfolio. So when you put all of that together and by the way, all that's IP is based on a very strong patent portfolio. We have over 13,000 patents issued, almost 18,000 patents in process. So very, very deep investment backed by its IP portfolio. But that's what creates our product fillers. That's integration capability allows us to attack data center, our client and gaming in our embedded markets.

And as Lisa said, we've expanded the aperture of semi-custom beyond that gaming center pieces had and now extended it, as you'll hear more from Salil Raje later for both data center and embedded applications.

Well, some of you will recall this slide. I actually showed it at our AMD Financial Analyst Day a decade ago. And what I talked about was really the bets that we were making, bold bets that we would set the future of the company on. And as Lisa said, these were tough decisions that we made on the long arc of development. So we bet on a new x86 CPU architecture, Zen to bring competition back to the CPU market. We called out the first stack in 2.5D lateral connection of chips, silicon on silicon to provide more performance GPUs in the industry.

And then our Infinity Fabric to give us a modular architecture to punch above our weight for that R&D investment that we were making and to pave the way for the partitioning of chiplets and our road map going forward. These were far-reaching claims at the time, but we put our heads down and we've executed. And so that's really a hallmark of AMD. It is our execution. We've delivered 5 generations of the Zen CPU. We split it now into high-performance versions, power and compact cloud-optimized version also used in our networks, but all maintaining consistency of instruction set architecture.

We went where no company was willing to go in the bet we made with chiplets when you look at our GPU road map. We invested not only in the 2.5D that I mentioned at that FAD but 3.5D, both that lateral connectivity as well as 3D stacking to create an incredible advantage for our customers because with that density of computation comes efficiency. And with that efficiency comes total cost of ownership advantage for our end customers. And this is what's driven a key element in driving our GPU market adoption.

It's also given us the ability to really reuse IP across the company and an agility for sometimes very late binding decisions we make when customers as they want to do, particularly our largest customers, needing us to tailor to their needs. Where are we today? We're simply accelerating. In this industry, you can't slow down. And it is our road map that is driving us to continue to innovate from rack scale for the largest of hyperscaler and sovereign AI implementations to, of course, our PC and embedded applications.

Our competitor is developing an AI area, more proprietary walled garden solutions. But we are leaning in going forward on commitment to open software and open hardware AI ecosystems. And Lisa talked about this incredible pace of scale, of expansion of computing to meet the demanding cluster needs, the growth of incredible computing that we need. And so that's where we're leaning in as well. We've got the IO technology that we brought in-house through SerDes, investments in our networking capability as well as the technology that we've done to link as we bring these clusters together.

And even more than in prior applications, security is critical. AI needs to know that your data is entrusted, the customers' data is entrusted, and we've extended confidential computing to AI clusters. Well, let's dive down a little bit. We'll start with CPU road map. Zen really reshaped what CPUs were delivering in the industry. If you recall, the industry had plateaued. Innovation had died off and Zen brought back significant performance improvements every generation, over double-digit percentage of performance and customer value that we delivered. This came with more core density, higher efficiency and economics that we brought, leveraging our approach.

You look at Zen 5, we re-pipelined for yet more performance with every core that we added. We have also added AI support. So people don't realize that vector engine in Zen 5, it's a native 512-bit wide vector engine, along with the software support that you needed is an outstanding inference engine for a number of applications that are not the most demanding that require GPUs.

Our next generation of Zen is Zen 6. Zen 6 is in our next-gen EPYC that you'll hear more about from Dan later in the agenda. It was the first tape-out in TSMC 2-nanometer, and it's going to further extend our leadership across our CPU, both performance and efficiency. And we continue to provide 2 core variants: one optimized for performance, one optimize for efficiency. And I talked about that consistency of instruction set architecture. We're now over a year in having successfully stood up with Intel, an advisory group that drives alignment of the x86 software or instruction set ecosystem, protecting those billions and billions of lines of code that customers have out there with x86, safe chasing their investment.

Let's talk about AMD Instinct, our data center GPU. Since MI300 was launched in late 2023, that marked a shift to an annual cadence of development. So with that, that meant CDNA, our GPU building block for the data center, also went to an annual cadence, driving generational improvements in compute as well as memory performance. CDNA 4-based MI355 is ramping incredibly fast right now with expanded AI math formats, including block scale for accuracy preservation.

Our CDNA 5 is what makes up MI400 series launching next year. Also expanded math formats for more efficiency and HBM4, bringing leadership memory capability. And just like the CPU, which remains on an alternating design teams, everything through Zen 6, even beyond Zen 6, Zen 7 and design, we do the same thing with our GPU. And so we have MI500, our generation next, next, well in the design phase, driving yet more compute, memory and interconnect technology. And what has happened with this annual cadence that we have, it's a requirement, just given the pace that we see of that explosive insatiable demand of AI computing.

If you look at this curve, it's showing flop rates. So that's the computation rate that we have in our GPUs. And prior to 2025, you can see that we are on a doubling pace of that flop -- of those flops every 2 years. After 2025, it's doubled. We're on at 2x every single year of that capability. And that's with a baseline of 16-bit math, one of the more predominant math formats. But again, optimized math formats yet again increase your performance. And so what you see is on top of that baseline at 2x every year, FP8, which is 8-bit math, provides a doubling again of that baseline. And FP4, 4-bit optimized math doubles yet again. This is a compounding exponential growth rate of computing that our industry has not seen before.

So I mentioned earlier SerDes, and it's really the first time that I've done a deep dive on SerDes, and it tells you about the expansion capability and connectivity that's needed of our industry. So SerDes are front and center. We saw this coming. So what we do is started -- you look at the standard NIO, PCIe. And back in several years ago, in 2022, we in-house the design of that PCIe Gen 5 because we needed more performance, more optimized implementation because when you look at the math of how that's deployed across our portfolio, in fact, that implementation has actually already shipped tens of millions into the industry.

Well, we've got further skills because upon the acquisition of Xilinx several years ago, we immediately brought the SerDes teams together, and that brought 112 gigabit per second interconnect technology that was used in networking right into the AMD portfolio. And so that's, of course, shipping already today in the Versal FPGA, but it's also the SerDes underneath our PCIe Gen 6, which powers our next-generation server code named Venice.

And now you look at where we're going in the MI400 series in that Helios rack that Lisa mentioned, we doubled to 224 gigabit per second technology. And that's critical for our scale-up capabilities, and I'll expand on that in just a minute.

Where is the industry going? It's not slowing down. Copper is going to 448 gigabit per second. But in that signaling technology, when you look at the number of wires you need for the bandwidth we need and the power dissipation, it's going to start to transition at rack level to optics. And so we're preparing both technologies in parallel, both the 448 gigabit per second copper as well as investments and road map that we have for optical. So we will manage that transition.

And then I always love talking about the Infinity Fabric. I've often called it the hidden gem that we have. And you look at what we're doing, we've continued to innovate, and that's what gives us the scale that we have to be able to power these leading edge, #1, #2 supercomputers in the world. The growth that we've had in hyperscale applications across both inferencing and training rely very, very heavily on the ability to seamlessly add cores and have that performance scale and then intercorrect from chips to socket to rack and in fact, even to scale out as we build out the clusters.

When you look at what we did with our third generation, it powered our first leading supercomputer, the Frontier system. Now it's #2 in the world because the fourth generation expanded its capabilities to power Frontier. And Frontier is the currently the #1 supercomputer in the world, but it also showed that versatility of the Infinity Fabric. That baseline of the MI300 that you see in that Frontier supercomputer was swapping out chiplets, leveraging the versatility of this IP, you have then the MI300X, which powered our AI system development and the MI300A and then the MI300X, which is a CPU-only version, which is powering Microsoft Azure highest bandwidth CPU that they have in their fleet.

So CPU-only version, CPU integrated GPU version and a GPU-only version, all with the versatility that we provide with our fourth gen and beyond Infinity Fabric. And that leads us to fifth gen that we're introducing today. It's poised to deliver for the MI400 series next year with the Helios Rack. It's optimized for best-in-class efficiency across chiplets and sockets, delivering the membrane bandwidth that we need to support that advanced system, and it's engineered to support open standards. And of course, like our other IPs, we're well at work on the generation beyond 5th.

And now I want to talk about chiplet and packaging, which has been a historic strength for us. We've been a longtime leader in this area, delivering differentiated solutions that have allowed us to push forward beyond the effects of a slowing Moore's Law. We're now in our generation, but you have to remember the beginnings. When we started with our first generation EPYC, we implemented, as you see on the far left side of the slide with chiplets, 4 distinct chiplets that we brought together. And it started us on a path of really leveraging that partitioning capability.

You look at later in the server road map, we changed that partitioning to be separate compute dies and IO dies. And we started stacking 3D V-Cache. It gives us an astounding performance capability. In fact, we're in the fourth generation of that stacked SRAM, that stacked V-Cache still without a competitor in the industry to that capability.

On the GPU side, this packaging, this chiplet and packaging capability has been critical for us. It's what delivered for us with the 3.5D that I described a moment ago with MI300, that flexibility that I described and an incredible density, giving us the TCO advantage in our 300 and MI355 road map. But we take it even beyond that for MI400 series next year. We actually have a doubling of the packaging interconnect density. And so that gives us an incredible silicon packing capability in the next generation.

And so when you go back to that start of the first EPYC chiplet implementation to what we use for the Helios system launching next year, it's a 30x increase in the silicon available for compute and memory on that design. Just a significant impact that we've had with our road map and our implementations.

Well, look, I want to show an example. All of this technology is nothing without its ability to come seamlessly together in a solution, and Helios is that best example. It's proud to show off the capabilities that it has as it brings together our next-gen CPU, our next-gen GPU, our next-gen interconnect technology, our next-gen SerDes, all of those technologies I just described come together to give us the link technology for -- the compute and link technology that we need for leadership capabilities.

And it also highlights the co-optimization that you need when you're at an era of rack scale. ZT Systems, the design team of ZT Systems was a huge addition for us in optimizing across all of those elements for a performance per watt across those compute elements right through all of that interconnect for leading-edge efficiency. And it goes beyond that, requires software co-optimization as well.

So the ROCm facets of the communication libraries and the optimization of the workloads also are co-optimized at rack level. I'd love to pull out a compute tray and just give you a visual of how this all comes together. What you're seeing is our foundation IP as well as the glue IP coming together here to have an incredibly dense sled or compute tray that we build on to create that Helios capability. You're seeing incredibly tightly coupled GPUs. You're seeing those connected leveraging our Infinity Fabric at 112 gigabit per second. You're seeing our ability to use standards.

So it's a UA link, which is connecting those GPUs to our AI NIC. And is UALink packetized and run over Ethernet that's giving us scale-up capability in the Helios system. And there's one thing that you don't see as you see the physical elements here, but I described it earlier. And that is those security blocks that I mentioned earlier, of course, embedded in all that elements. And why is that important is that we're now delivering security and confidential computing to AI cluster level. And why is this important?

Well, it becomes so important right now because what does AI do? As you are running your company and you're building models, you're training your data, all your data that you spent decades building up and creating weights, they become your crown jewels for your company, and it has to be safe chase. So what this capability does is it provides encryption. It's an end-to-end encryption. It's taking -- encrypting data that's at rest, data in motion. The keys are owned by the user. You get it from a key server.

And it is ensuring that any visibility in that trie traversal of the data, all you're seeing is encrypted information. And so this is enabled with the fifth gen of EPYC that's already shipping today. It's a standard called TDISP or Trusted IO and you'll see in the AMD portfolio next year and with other industry partners, we now extend this to heterogeneous clusters, CPU, GPU, storage devices, networking devices, all protected with this important capability.

Well, now I want to shift gears a little bit after going through a set of IPs and the example I showed you for data center and cluster applications. And I want to talk about our gaming GPU road map. We've been on a continuous journey for driving graphics capability and now AI capabilities with our Radeon and Ryzen road maps, and is powered by the RDNA IP. RDNA 2 had brought a ray tracing optimization to bear. But in the next generation in RDNA 3 Tensor engines were added.

So as the introduction of AI to even further improve upscaling capabilities, giving you more life-like images. Well, with CDNA 4 shipping today, we have even further usage of AI, and it's doing a couple of things. One, incredible upscaling. So you don't have to look beyond FSR 4. If you haven't -- if you're -- if some of you are gamers, I'm sure if you run FSR 4, you see an incredible gaming experience while upscaling it's a beautiful visualization. And it's not just about improved gaming experience going forward in this road map. And you'll hear more about it from Jack going forward.

But as we further improve all of that machine learning for our ray tracing capability, it also enables these devices for edge AI applications. And then, of course, our AI engines, our Neural Processing Units. We announced the first dedicated MPU accelerator in CES 2023, and it began shipping later that quarter.

Our second generation of MPU was focused on performance per watt improvement, more tops, of course, in each generation, but it really unlocked the capabilities in Windows Copilot Plus. And so these are all powering our AI PC shipping today. And then our third-generation MPU, well in design is going to be another significant improvement in both TOPS, processing capability, but even more energy efficiency. And that's not only important in an AI PC that you have in front of you, the battery life you gain, but vitally important for the edge AI applications, which you'll see growing in application with this technology.

As we wrap the IPs that really bring our solutions together, we've saved the best for last because when you think about AI, it's software, and it's our ROCm stack that is the foundation of giving the experience and the ease of deploying on AMD. And our ROCm stack is now fully battle tested. It's been battle-tested in high-performance computing in those top supercomputers. It's now been battle-tested by the most demanding hyperscaler customers.

But we've also expanded. We've expanded -- if you look at the last year, our adoption in enterprise is going up, and therefore, the ROCm support for enterprise as well support for the open community and bringing the additional power of developers across the world behind the ROCm subsystem.

So when you look at this capability, what you see is that we now have ROCm as that common foundational IP tying together the AI experience from data center and those most demanding applications to gaming applications and edge AI that you can run on Ryzen and Radeon and right into those embedded APUs that we have and is a growing part of Salil's business. Tremendous progress on our ROCm AI stack.

Well, look, I want to wrap up with one key message to you is that as we've implemented all these technologies and partnered so deeply with our customers and driving the market share gains that we've had, it's done something else because we've become a trusted partner with our customers, and we hear clearly from their standpoint where the market is going. Data center explosion, you've already -- you know that, that's amongst our largest opportunities right now, and we're addressing that with the rack scale investments from GPU scale that we've made.

Agentic AI. Agentic AI and the processes that kicks off, it turns out are driving much higher CPU workloads, whether it be retrieval augmented generation, the test phase of a research AI application you're running or simply Agentic agents kicking off many traditional scale workloads on GPU. We're seeing this as a major, major growth vector. And likewise, the edge AI explosion is starting now, and we are poised with our integrated CPU, GPU, NPUs to take advantage. So those 3 trends are upon us now. They're going very rapidly, but we're also super excited.

I'm personally very excited on Agentic AI. We start already with robotics today, but it's going to be a vastly growing market and quantum computing. Our FPGAs are used today, predominant to implement leading-edge error correction. Our FPGAs are also controlling the multistate quantum of many, if not most of the quantum developers. But what actually gets me most excited is what we're doing to bring CPU and GPU class of computing -- classical computing and optimize it with the quantum computer as an accelerator. And super pleased with the work we started with IBM and the partnership we've announced, and we'll expand that with other quantum providers over time.

So look, I just want to wrap by telling you that AMD is absolutely fueling with innovation that insatiable demand of computing for the AI era. Our road maps, our design to consistently provide product leadership and to leverage that broadest of IP portfolios. The open software and hardware ecosystem that we have, giving both diversity of solutions and choice to our customers; and lastly, our relentless execution.

We are collaborating with our customers and ecosystem partners alike to provide differentiated solutions and leadership products. Thank you very much. And with that, I'm thrilled to bring up my close partner, Forrest Norrod, our data center leader.

Thanks you very much. It's a pleasure to be here. Thank you all for coming. I started at AMD 11 years ago, shortly after Lisa became CEO. And the mission that she set for us was to reenter the data center market not to participate, but to be a leader. And at that time, we were facing a dominant competitor that had nearly 100% of the market. And so we knew that in order to make headway to fulfill this mission that we would have to build a strategy that incorporated a leadership product road map, a long-term strategy to rebuild and build customer trust and a product and solution set that was truly differentiated to induce people to give in the consideration and then to make that switch.

Now we implemented that road map based on our understanding of where the data center industry was at the time and what would happen to it over the next decade. We knew that virtualization in cloud would continue to grow, that was clear. We also saw that the explosion of data was going to continue unabated, that the conversation would shift from terabytes to petabytes to zettabytes of data flowing around the world's IT systems.

And we also saw at the time the beginning of heterogeneous compute, the beginning of the era of the CPU and the GPU collaborating together on HPC and early AI applications. And so we built a road map that differentiate in areas that were relevant to those key trends. Higher core densities, leveraging our chiplet technology, higher memory and I/O bandwidth, more flexibility in both, all things that were directly relevant to where the industry was going.

And so we built a multigenerational CPU road map, and Dan will talk a little bit more about this as well, that steadily increased its differentiation every generation and took more and more leadership with each new CPU, such that over time, we have grown our market share in server CPU to over 40%, were the de facto standard in the cloud, and we're viewing this 40% as Dan will tell you and Lisa already hinted as just a milestone. We are far from done with growing in the CPU.

But now we face today a new dominant competitor, a different dominant competitor and a different changed future of the data center. Clearly, AI has changed the data center substantially. The AI factory, the transformation of the data center into the AI factory is obviously the most urgent trend to produce systems at giga scale that can efficiently train and efficiently infer to deliver business value from AI. And that's happening, of course, at unprecedented scale. We all know it. It's embedded in all of the discussions we've just had even on the TAM. And so that unprecedented scale means performance and power in which we've historically excelled are critically important.

But even more so, you need reliability and resiliency to ensure that when you're building systems at that scale that they can actually sustainably deliver the value that the customers need. One thing we don't see, however, is we don't see a monolith in the data center. We don't see that the data center is dominated by 1 particular element. Instead, we see GP racks is absolutely central to the data center, of course, to power the LLMs and power the AI engines at the core of these AI factories, but they're useless without the sea of data that fuels AI without the storage systems to give them the data to train, to give them the data to infer, to give them the data for agents to actually operate.

And then, of course, those agents, the tools now that LLMs use all of the databases and other systems of records that need to be accessed for LLMs to actually deliver business value run on general-purpose compute racks. And so we see a rich and vibrant data center in order to deliver these AI factories.

And so we built our data center portfolio over the last few years to address these needs. Vamsi and Dan will talk about our GPU and CPU road maps in just a moment and the software that makes them sing. And then I'll come back in a few minutes afterwards and unpack our networking strategy and tell you more about how we're putting all of these elements together into compelling system and cluster level solutions.

So with that, let me first introduce Dan McNamara, the SVP of our server CPU business. Thank you.

Good afternoon. So 3 years ago, at this forum back in Santa Clara, I laid out a vision on EPYC, and I'm relay that, we really believe that we were a strong contender in server CPUs. And we felt like we were going to really go on a new era of growth. And I'm super excited to be standing here today and tell you that we have moved from that strong contender to truly the de facto server CPU leader in the market. And here's why.

We delivered on the strategy we rolled out. Since then, we've delivered 2 new leadership generations of product with EPYC, with the best architecture, the best advanced packaging and advanced processes. We've doubled down on our customer and partner network, building a vibrant ecosystem. And as Mark just said, we've continued with our maniacal and relentless focus on execution in creating a predictable cadence of products delivered to market. And we've built a very good amount of trust in the marketplace, but most importantly, all of this has enabled us to accelerate our path to number one, server market share.

Now let's look at the driving forces going forward. So Lisa and Forrest both talked about the fact that we've been on a great journey that we have, and we're hovering around 40% share. But what's more compelling is that we've never been in a better technology and market position and at a time when the market is inflecting with AI demand. And if you think about it, it's really because we focus on the key needs of each of the segments that we operate in with cloud is very well known. It's high density, performance and efficiency and driving generational TCO gains. And we've done that repeatedly through 5 generations, and that's driven a fair amount of share for us at this point.

In enterprise, it's a little bit different. You have more performance for core memory per core, trying to drive consolidation and modernization for the customer. And that's critical. In addition, though, there's very beefy enterprise workloads that you must have that performance for to run the enterprise. And then lastly, for HPC, it's all about flops per node. And we continue to drive floating point performance to drive key workloads like oil and gas discovery, genomics in that area, and we continue to lead in that area also. And lastly, as we talked about, AI is a horizontal, and we will continue to optimize for performance in TCO across the various used cases that I'll talk to you about.

Now let's spend a minute or 2 on the road map. And Mark hit this, right? Our foundational element for our products, every time we start in planning is to show, Lisa, a couple of different things. What's the density, the performance gain and the efficiency gen-on-gen. That's table stakes. But as Forrest just said, with every generation, we make deliberate decisions from customer feedback and market demand.

And you heard Mark say about Naples, we started in 2017, but with Rome, we drove a chiplet strategy that doubled the thread density and generated the highest-performing CPU in the market. Broke through cloud very strongly, enable us even in the enterprise and some high-end workloads and obviously kept us very strong in supercomputing.

And then with Milan, we doubled down on performance per core and drove an IPC gain and we basically drove an inflection in the enterprise for us across both vertical/industry workloads and general IT modernization. And then 4th gen, we came back and the market gave us feedback that we needed to do dual-core optimization. So we drove very high-performance core from Genoa, and we also drove a very high density and efficient core with Bergamo. And both of those are shipping in very, very high volume even today.

With Turin, we did more. We brought more density, more performance efficiency. We kept the dual cores, but we did some AI optimizations. We added to the AVX-512 block. We added a market-leading 5 gigahertz Fmax part for two things. One is for EDA workloads. But the other one is -- was for CPU host to GPU nodes and clusters for either inference and training, and I'll talk about that.

And then we are very, very excited to deliver Venice next year with more density, more efficiency, more AI optimizations and driving the best performance per system watt.

So now let's talk about the segments we service. One sentence for cloud, the cloud runs on EPYC. And we've been at it for a while with cloud, and it started with Rome. And we've expanded our offerings and optimization points for the various different workloads. It starts with traditional lift and shift, which was really the beginning of cloud, those workloads like ERP and e-mail and online transaction processing, that's all about TCO. And we deliver 50% performance per dollar better than our competition. And then there's mission-critical workloads for both enterprise and cloud-native companies, think of content delivery, think of collaboration tools, think of database and analytics. That is all about VM performance.

And there, again, we deliver over 70% better VM performance than our competition. And then what's been emerging for the last 3 to 4 years is HPC in the cloud. And these are traditional HPC workloads that are being accelerated by 2 different products, the high-frequency SKU that I talked about, but our X3D over a terabyte of L3 cash, accelerate simulation performance by over 2.7x and in some cases, 3x. So you see the cloud has been -- we've been accelerating for the -- with the cloud right from the beginning. But more -- lastly on this is enabling all of this is enabled for our security features for hybrid cloud adoption and multi-cloud adoption, which is very, very important going forward.

So all of this value Lisa talked about, we've earned the trust of industry leaders. Top 10 of 10 social have their platforms deployed with EPYC. Largest cloud-native companies deployed with EPYC. But one of the more interesting things that I'd really like to talk about is the Fortune 500 mainstream enterprise customers are now in their hybrid and multi-cloud environments, adopting EPYC and third-party IaaS faster than anyone. We've seen a 3x adoption this year. And what that does is all of that drives back to the on-prem and drives enterprise adoption on-prem so that the hybrid multi-cloud is end-to-end on EPYC.

And speaking of enterprise, we've been very, very focused on enterprise for a couple of years, investing very heavily, but -- and one of the key focus areas has been our ecosystem build-out. So I'm very excited to say that our platform growth is 3x over the last 3 years. We've got almost 180 platforms from racks to blades to towers to edge devices. We've got 3,000 solutions in the market on top of those platforms, another 3x growth from the last time we had this forum.

And our OEM and ODM channel is very, very excited and has shifted most of their work to be time to market with us on every different launch. Secondly, one of the areas where we break into the enterprise is what we call industry or vertical workloads. And these are the -- what I would say is these are the workloads that drive the end business. So in semiconductors, that's EDA. In telco, it's the network. And the goal there is to accelerate those workloads in either driving more throughput or drive faster time to market or faster time to results. And we almost double our competition in terms of faster time to results.

And then lastly, core IT modernization. That is all about driving TCO savings. And with our latest generation, we have an 8:1 consolidation factor from our competition that along with our density and performance drives up to 80% TCO savings for the enterprise. So all of this is accelerating the adoption. And we look at 3 things when we think -- when we track our adoption.

First is top customers, are they using us? As you can see here, over 60% of the Fortune 100 are using us. And that's growing quarterly. We track that very, very closely. The other thing is, are we getting new customer acquisitions? These are the first time with EPYC in their fleet. We've doubled that year-on-year. And then probably the most important is the customers who have used this in the past with the either Rome or Genoa or even turn now going forward, are they using us more? Are they expanding their uses of EPYC in the fleet? And again, we've seen double the consumption year-on-year.

So the enterprise conversation has changed quite dramatically. And we're very, very excited to see that change because we've been investing very heavily here for the last few years.

Now HPC. It's sort of our legacy with -- from Naples, right? We've been winning over the years with EPYC and even instincts across national labs, research institutes across some of the most important workloads like genomics and oil and gas and now it's shifting towards AI for science. We are in 1/3 of the top 500 supercomputers. We're 12 out of the top 20 green supercomputers, and we continue to evolve our solutions. And you're going to hear about this as HPC evolves to AI for science and Vamsi will talk about this, with our MI430 and 450 in Venice, we will continue to drive more and more performance there, but also provide our customers, the much-needed silicon diversity and open ecosystems to really participate and enable this market going forward.

Now with AI, I did want to spend a couple of moments here because I believe there's a thesis out there and Lisa mentioned this that CPUs have been cannibalized. And I want to spend a minute and tell you what we're seeing because it's the exact opposite. And there's a number of different reasons and applications why this is happening. And first, probably the most well known is the head note, right? The CPU host to the -- in a 8-way server, GPU server. And what we did there is we saw very early on, and we built a turn product to drive very, very high frequency, a solution. And the reason is the server in this mode in the cluster is really more of a synchronizer an orchestrator. It's not -- it doesn't have the biggest job. But if you can drive high IPC and high frequency and keep those GPUs fed with kernel launches and all the orchestration, you can drive overall performance up on the cluster. And we've showed that over and over across both training and inference.

Secondly, probably the biggest conversation I have through the course of a week is just creating an AI-ready data center. That's driving consolidation. That's driving energy efficiency. That's creating more space as our CIOs or our customers are trying to drive how they go, develop an AI infrastructure and modernize for the future. This is front and center how do you do all this, but also enable those most critical enterprise workloads to boot.

And then the end-to-end AI pipeline is also expanding quite rapidly. And you have pre and post processing that's happening and it's driving all sorts of new workloads like RAG and ETL and new storage workloads and then database of more analytics. So all of that today in the enterprise is on x86. And then CPU inference with the explosion of SLMs being fine-tuned for domain-specific applications, the CPU is now playing even a bigger role in inference because it's a cost-effective way to infer on a smaller model. It also is in a mixed workload environment because CPUs actually have multiple jobs with our high core counts leveraging the extra core counts there for inference, real-time inference has gone extremely well. We have many, many examples of that.

And then lastly, agentic and autonomous agents. And Mark mentioned this. But as the agents grow more and more CPU cycles are needed to drive the interfaces between the agents and all of the real-time enterprise applications and the enterprise-wide structured and unstructured data is critical to drive this interconnect. And you've all probably heard of the concept of an MCP server. So see all of these 5 applications and usage models for CPUs is driving what we believe is a very strong inflection point in CPU TAM.

As you look here, we're going to add $30 billion in CPU TAM over -- by 2030. It's basically doubling the CPU TAM in this horizon. And as you can see right here in terms of '25 to '26, the inflection is right now, right, with the agents growing, with SLMs and all of the things we've been talking about on inference. So very exciting for us because I'm coming back to what I said earlier, we've never been in a better position from a product standpoint with Genoa, Turin and headed to Venice.

And then speaking of Venice, we're going to launch that next year. It's on 2-nanometer TSMC process. It's looking great. Our cloud -- our ODM, OEM and cloud partners are all bringing up systems and looking very, very good. We believe we will have the biggest ecosystem at launch next year when we bring that out. And not only does this maintain our leadership, this actually widens the gap from our competition. We cannot wait to launch this product next year because it will absolutely be part of this -- that curve that I just showed you.

Now let me close with where I started. The execution of our strategy has got us to this point, and we are in a great position to continue the acceleration of the leadership. We will continue to drive our cloud expansion. We're hitting the tipping point in enterprise in that we expect to grow, and we are extremely well positioned for this AI growth. And there's not a question in our mind that we have a clear path to greater than 50% market share.

So thank you. And I'd like to introduce Vamsi to the stage.

Good afternoon, everybody. I am super excited to be here to talk to you about all the progress we are making on our AI strategy. Now one of the real privileges with my job is that I get to talk to AI innovators across the world, people that are doing incredible things, inventing new drugs, solving new math that has not been solved before, tackling really, really hard problems. And as we've made progress with our strategy, what's been really amazing for me to watch is that more and more of these breakthroughs are actually happening now on AMD systems. Whether it's researchers at OpenAI or developers of Meta or enterprises like Tesla or numerous startups that are doing exciting things on our platforms, everybody is now proving that they can do their most advanced AI development on AMD platforms. So what I want to do is tell you a little bit about our strategy that's making all of this possible.

Our strategy is based on 3 core pillars, right? First, delivering leadership road maps; second, providing world-class open source software; and third, establishing deep partnerships with the leaders that are defining the future of AI. And as we made progress executing to our road map, as the open source community embraced AMD, as we establish the right routes to market, more and more adoption came.

So I want to give you guys a little bit of a sense for our progress with a few examples. Just 18 months ago, if a user wanted to go develop on top of an interesting platform, well, there's no place in the cloud, they could go to. Fast forward to today, we have like a dozen clouds now. We have 35 platforms that carry AMD GPUs.

Similar story on the software side. A couple of years ago, if you went to the leading AI frameworks, and said, "Hey, I want to develop on AMD," support was not guaranteed. We've fixed that. We worked with all the top AI framework systematically and said let's support AMD GPUs. A good example is the partnership we set up with Hugging Face 2 years ago. At that time, Hugging Face model support for AMD GPUs was not guaranteed, but we work with them to make sure that, that is fixed. And today, all of the Hugging Face catalog of models run AMD GPU successfully.

So as we made progress, more and more workloads came and customers started adopting, whether it's Microsoft with their GPT model or Meta with their recommendation models or X with their Grok models, customers are trusting their most important workloads today on AMD platforms.

So now following the success of our MI300 products, this June, we launched our MI350 series of products to market. These GPUs were launched to address the huge demand in compute that existed and brought with them innovations like 4-bit compute, the next-generation memory system that allowed AMD to stay at the leadership level on memory capabilities.

And most importantly, we made it super easy for people to migrate onto this platform, both from a hardware perspective system configuration wise, but also from a software perspective. And that's why the ramp for these GPUs has been smooth, and they're delivering excellent performance.

As you look at inference serving on leading models like GPT, OSS or Hunyuan Video, these GPUs deliver excellent performance. To look at how easy it has been to adopt this platform for critical workloads like GPT, when it first came out in August, the latest GPT OSS model, it ran on day 0 on MI355 just a few short weeks after we went to production. And we introduced support for 4-bit processing and delivered excellent performance. As you see on the charts here, this is also a very good training machine, particularly for workloads like fine tuning.

Now all of this performance translates into true economic value. When you look at metrics like cost to serve 1 million tokens, this is actually an example from a recent Inference max benchmark that was conducted, the latest GPUs, MI355 on the GPT OSS model deliver up to 10x benefits in terms of the cost to serve tokens relative to our previous generation of GPUs.

Now this is exactly the type of economic value and gains that our customers expect generation over generation. And as our platforms have made progress and expanded in their capabilities, so have the workloads that have run on them. As you might remember, our initial focus had been on inference that leveraged the strength of the MI300s memory system with GPT being one of the early models, but since that time, inference deployments have expanded in terms of workloads. We have added recommendation system support. There are multiple modalities that are supported and coding agents that are supported. So a number of inference models and workloads have now come on to the platform.

We've also steadily expanded our training engagements. Many customers, Microsoft is doing MOE training on it. Meta is doing recommendation training on it. Our own teams are building European language models, and many exciting startups like Essential, like Cohere, like Zyphra, they're all training their next-generation model architectures on AMD platforms.

Just to give you a sense for like how the customer journey has been in terms of workload expansion, I want to tell you about what happened with one hyperscaler engagement. They started, obviously, with the first workload, very successful. And just within 12 months, within 1 year, they now have over 70 workloads spanning all of what I talked about that is now running on Instinct platforms. We're super thrilled that they're able to do these sorts of things.

Now as much as all of this adoption has been because of our compelling road maps, it's also been because of the tremendous gains that we have made building our software capabilities. So I want to talk a little bit about that. So if you look at ROCm, with each release, as we have added significant features and performance, that's what has been crucial to supporting all these workloads.

We also recognize that we needed to be relentless in our focus towards developers, improving out-of-the-box capabilities, providing more richer collateral content, documentation that makes them productive right away. Our customers are actually delivering AI capabilities at unprecedented pace. We responded to that. We've accelerated our release cadence.

New optimizations, new capabilities now ship every 2 weeks in Dockers and pre-packaged Containers. We have ensured that the leading models are supported on day zero. When DeepSeek came or GPT OSS came or the latest Qwen or JAMA models came, they all ran in day zero in the last several months.

So adoption of the platform because of all of these improvements that we have made has been growing significantly. And that momentum is showing in the numbers. 2 million models that exist on Hugging Face today all run AMD GPU platform successfully. The top 10 AI frameworks and projects for GPUs all have very good AMD GPU support. And if you look at metrics like downloads, they are on a fast exponential. Compared to last year, we have 10x the amount of downloads.

There is a growing and vibrant ROCm ecosystem. And we are doubling down on developers and the developer community even further to make it go even faster through collaborations that we have set up with research professors at Stanford and at Berkeley, through rich educational content that we're delivering in partnership with organizations like Deep Learning, we've also stepped up our engagement with the developers with a number of forums like Hackathons, contest and meet-ups and so on. If you see in the last year, we have significantly stepped that up. An outstanding request that our developers have had is, "Hey, can I go to a place where I can go hack on AMD GPUs?" And so we provided in June this year, a developer cloud that's been very successful, and we are committed to expanding that even further.

Now all of this progress, obviously is not happening as an accident. It's because we are driving a clear and deliberate strategy. This strategy is actually pretty simple. It's based on 2 core principles: build with open source and build with the right levels of abstraction. So let me talk about both of them.

When I talk about building with open source, open source is where we have scale. And it also moves extremely fast. And that's where AI developers live today. And that's why we have chosen to do a lot of what we do in ROCm in the open, pretty much our libraries, our framework cores, it's all out in the open, allowing people, customers and developers to contribute to ROCm. So that's been a big plus for us.

We also leverage the trend towards rising abstractions. Let me give you an example of what I mean by that. I have a -- I'm a computer science guy. I have a PhD in computer engineering. If you give me one of our chip that has like 100 billion transistors and said, "Hey, write some assembly code," it's very, very painful to do that. Engineers don't like doing that. That's what has led to programming abstractions like PyTorch, right? People like to write higher level code and makes them more productive.

So that's what we have done. We have enabled very strong support for all these frameworks that are at the right levels of abstraction. That is the trend that is going to happen. The happy byproduct of that trend is one such code exists is nicely portable across platforms, and we are benefiting from that. But the thing that I'm really excited about our software strategy is actually what's coming next.

Over the past several months, many times, my engineers have come and told me that, "Hey, we've used AI to write this GPU kernel and it's shockingly better than anything that we were able to create before." So what is really happening is the enormous gains that AI has provided to general purpose programming is now coming to GPUs. While it's not yet fully there today, I am deeply, deeply convinced that a significant amount of GPU programming is going to be transformed by AI. A lot of GPU programming will be done by AI, and it will take down with it any last remaining barriers for the adoption of our platforms.

So now as we've made a lot of progress with our platforms, with our software, we have learned a lot from our customers about how their workloads are evolving, about their road maps. We have literally earned a seat at the table as they are designing and planning their next-generation systems. And we have incorporated those learnings, those precious insights into creating our best GPU family, the MI400 series. This is a defining moment in our AI road map journey.

Built on top of the latest in process technology, it leverages all of our innovations that Mark talked about, the chiplet architecture and 3.5D packaging, the MI455x product in this family packs up to 40 petaflops of peak FP4 compute. It has 432 gigabytes of HBM memory running at 19.6 terabytes per second. It is the best and most advanced AI accelerator that we have ever built, designed to serve trillion parameters models that are the reason the next generation of AI infrastructure is getting built.

We've also synthesized all we have learned about our hardware, about our software, about our networking and about how we bring it all together into a rack scale infrastructure with our Helios offering, which Forrest is going to unpack after me. This rack scale architecture is designed to service the most demanding training and inference workloads.

With its leadership memory system, strong compute performance and rack scale architecture, this product is designed to offer leadership performance and true TCO value proposition, truly differentiated TCO value proposition for our customers.

Now the MI400 series is more than the MI455 product. Even as AI has continued to transform markets, there has been a huge explosion in demand for scientific computing driven by nations seeking self-sufficiency in their compute and also research institutions and enterprises that want to make scientific breakthroughs. To address this exact need, we have built the MI430 product. So what the MI430 product is actually built out of the exact same hardware and software foundation of the entire MI400 series. It leverages our chiplet architecture. But it customizes the compute for what these markets need and provides the high persistent double-precision floating point compute that is required for getting the highest performance for these markets.

This product continues the leadership tradition that we have. We have the fastest supercomputers today with Frontier and El Capitan, and the 430 series is designed to continue that into future supercomputers. You can stay tuned for more around this project. We're going to share a lot around this product. We're going to share a lot more about this at the upcoming supercomputing show next week.

So now the MI400 series, our customers love it. Right, whether it's Frontier builders like OpenAI or Meta, or infrastructure partners like Oracle or research institutions like Oak Ridge. There's been a significant interest and strong adoption for these platforms. Lisa talked a little bit about our engagements. I want to give you my own perspective, how this has gone.

Let's start with OpenAI. A couple of years ago when we first engaged with OpenAI. It was not clear how their workloads were actually going to get mapped to AMD GPUs. So we decided and we sat with them and said, "Hey, it's super important that we actually support Triton, which is a chosen framework that they use and say, we need to add AMD back and support for that." So we enabled it functionally. And over the last year, we made it performance with adding a lot more features and capabilities. And as they gained more confidence, they have expanded their engagement with us from hardware, from software, from networking, and that led up to the MI400 series. So we're super excited to see what they would be creating with this product.

We're also deeply grateful for our engagement with Meta. Starting with our MI300 series with Llama where they served and showed that it's great for that reason and expanding that to recommendation models. We've collaborated with them very actively with PyTorch and deep collaboration with Helios that Forrest is going to talk about. When AI leaders that are defining the future of compute choose AMD, it absolutely validates our road map, our ability to execute and to be able to be deployed at scale.

Now as amazing as the MI400 is, what seems to be even more extraordinary is the demand for compute keeps going up. As models become larger, as models become stronger, as they become more diverse, they become agentic, they think, they interact as industries adopt them more, there is continued need for a lot more compute. And to meet this extraordinary demand for compute, we are introducing our next big leap, which is the MI500 series.

With significant innovations in compute, memory, networking, rack-scale integration and even deeper hardware and software codesign, this is not an incremental step on our road map. This is going to be our next big breakthrough in AI performance.

So now to bring it all to a close here. We are at a truly unique moment in our AI journey. Our leadership road maps are getting stronger, delivering greater gains with each generation expanding our usage. Our open platforms with our rack scale systems and open source software are driving greater adoption. The deep partnerships that we have set up with the AI leaders that are defining the future of computing are showing what is possible when you collaborate at scale. And the MI400 series is poised to place AMD in the middle of some of the largest AI infrastructure build-outs that will define this decade.

Together with our partners and customers, we are thrilled to be able to build the future of compute. Thank you.

And with that, let me welcome Forrest back to the stage to tell you how it's all going to be put back together at the data center level.

Thank you, Vamsi. Well, as Dan and Vamsi showed you, we are committed to driving leadership in the CPU, GPU components and then, again, the software that makes them sync. Now I want to talk to you about how we put it all together with the networking and, of course, with the system design at the rack and cluster level to truly build complete AI factories.

Let me begin with networking. In networking, as much as GPU and CPU now drives the performance of AI factories, the amount of data that has to be moved around to feed AI, to act on the results from LLM engines is absolutely incredible. And you have to have the networks that can handle it. And the networks needed for an AI factory have rapidly evolved. From the old days of a single network, we now see that you have to have a front-end network and up to 3 back-end networks to effectively scale out the GPU systems to reach Giga scale. And our approach to providing these networking solutions is a little bit different from our competitors.

First off, we believe in open. AMD always has. We believe in open standards, we believe in open ecosystems. And so at AMD, we've been thrilled to help spearhead some of the most significant new networking standards over the last few years with the Ultra Accelerator Link and of course, with Ultra Ethernet. Ultra Ethernet is a great example of a standard that has rapidly made Ethernet the premier and unquestioned best scale-out fabric in the industry. And we've done this in conjunction with other leaders in the industry. And so offering our customers choice and enabling others in the ecosystem to add incremental value to the AMD open ecosystem of AI systems.

Well, let's begin diving deeper with the front-end network. And the front-end network is an underappreciated part of the puzzle, but in reality, without the friend end network, the user can't reach the LLM, the LLM or the AI agent cannot reach the data, the databases, the resources that it needs to actually turn tensor operations into intelligence. And a good front-end network can do more than just connect, it can actually add performance to the overall system to accelerate AI and cloud workloads by offloading key tasks, software-defined networking, storage access, storage abstraction and of course, security.

And security, as Mark mentioned earlier, is incredibly important in the area of AI and networking could play a huge role in ensuring that systems are secure end-to-end such as users' data and the AI models are always protected.

At AMD, we have a fantastic solution for front-end networks. The Pensando team at AMD has built the world's best DPU. We're now in the fourth generation of this processor. It has a unique architecture using P4 programmable engines that allow us to do stateful and stateless services of any nature for traditional SDN and security offloads, but also to provide services to offload specific data movement actions for the GPUs. And it does all of that at line rate performance.

So with the Pensando approach for both the front end as well as I'll describe, the back-end networks, you can actually deliver network innovation continuously evolving at the speed of AI without giving up performance.

Now on the back-end networks, I mentioned there's 3, there's 3 that we are talking about today. You have the scale-up network that takes a pods of GPUs, dozens to a few hundreds today and welds them together into effectively a logical GPU that can coherently share memory and results in a very efficient fashion and look like a large resource, large single GPU resource. You also have the scale-out network that is not as tightly connected, but allows you to efficiently scale, particularly for training out over hundreds of thousands of GPUs.

Now the new concept, candidly, over the last year or so is the concept of scale across where even scaling out to 100,000 GPUs is not enough. To reach giga scale, you have to bust out of the walls of the single data center hall and federate data centers or data center halls in an efficient, effective way.

For scaling up that welding together of dozens to hundreds of GPUs, what really matters is you have to have an ultra-low latency connection. You have to have ultra-high bandwidth. And you have to adhere to the data transfer protocols that GPUs want to talk. You don't want to put any friction in translating from the way that GPUs compute and want to communicate into the protocol that you're using to do so. And of course, you have to have not just a scalable technology to bring out to hundreds of thousands, above all else, you have to have reliability because you can't have a link flap or a single link going down, bring down your entire pod of GPU, perhaps running hours of training time or disrupting a critical distributed inferencing task.

At AMD, with the MI450 and with Helios, our solution to scale up is open. We are implementing the Ultra Accelerator Link protocol that efficiently provides load store semantics to the GPUs to allow them to efficiently communicate. We are transporting that protocol at 260 terabytes a second across a Helios pod of 72 GPUs via packetized Ethernet, ULE OE. It's very similar in concept to the emerging standard E.SUN, where we're trying to packetize various protocols using Ethernet to transport.

And in Helios, we then have the scale-up fabric linking all of the GPUs together over 6 redundant network planes to again, provide that not just bandwidth and performance, but resiliency to ensure that a slight predication in the network doesn't bring down the pod.

For scale out, we connect, again, hundreds of thousands of GPUs to work as one. This is an area where the industry really has innovated together with Ultra Ethernet and with similar efforts to efficiently provide advanced RDMA networks that improve performance and very critically, reliability over either InfiniBand or Rocky B2. But more than this, the AMD Pensando NICs that we use to connect to the scale-out networks, with their P4 programmability, offer us additional opportunities to accelerate performance, to offload communication tasks from the GPUs, to offload collective tasks from the GPUs, to improve compute resource utilization, and therefore, get workload acceleration.

They also allow us to innovate new features like multiplane support and advanced transport handling to further improve network TCO. We've seen the multi-plan support offering opportunities to reduce the scale out networking TCO by 50%. And then, of course, just scaling across.

Now this is where things get a little bit different. If you're scaling across to multiple data halls or across tens of kilometers, the latency and reliability of those connections is different, very different from within the data hall. And so the network has to be able to accommodate dramatically varying latencies and network perturbations. So you have to do adaptive path management. You have to do dynamic load balancing. And there's a variety of approaches that have been recently promoted to do this. At AMD, we think the intelligence to do this really is where the data is generated and consumed in the node itself. And so we have implemented scale across and began delivering it to our customers already with the existing Pollara 400 NICs for MI300s in the NIC itself, and we can effectively deliver scale across functionality and performance without relying on proprietary or expensive switch extensions.

And so the NICs that we have to provide the scale out and scale across features are our Pensando Pollara 400 and Volcano NICs for the MI300 series in the MI400 series. But true to our commitment to open, we also support customers to have choice. And so we support amongst others, Broadcom NICs as well. So the thorough series of NICs work very well with AMD GPU solutions, the Thor 2 with the MI300 series, and forthcoming the Thor Ultra series of NICs, which fully implement UEC also work extraordinarily well inside of Helios.

And so our system is not closed. So let's put it all together. Scale across means multiple data centers can be confederated together. Scale-out means you can combine hundreds of thousands of GPUs together using advanced Ethernet. And then diving into the rack, as Mark showed earlier, we're using UAL and Infinity Fabric to interconnect the CPUs, GPUs and scale-out NICs within the compute blades. And then we have Ethernet-based scale-up switch trays that take the UALOE traffic and switch them with very low latency, high performance Ethernet-based switches.

Those are interconnected via redundant cable cartridges that have short lengths and provide high reliability redundant connections interconnecting all of the switches and the CPU compute trays. Finally, the front-end network and the scale-out network emerge from the Pollara or Thor NICs and interconnect to the rest of the data center systems.

You put it all together and you had Helios, which as Vamsi said, we believe will be the most performant, most efficient, most serviceable AI factory building block when it comes to the market in Q3 of next year, incorporating all of our technology and doing so in an open way.

But just as we will continue on annual cadence for the components, we will continue evolving our rack level systems on an annual cadence as well. And so accompanying the MI500, we have the yet to be named revealed rack that will unpack more later. But again, it will offer more. It will offer more GPUs, more bandwidth, more capability, more performance and the next generation of our components. So stay tuned on that.

Important to understand, for both of these, AMD is not selling the rack systems. We are developing the complete solutions ourselves and in conjunction with our partners and then bringing them to market through leading OEMs, through our ZT Partners who are now owned by Sanmina Corporation and of course, through our other ODM partners across the ecosystem. And so they will all have time to market solutions for MI450 and when we bring it and Helios to the market.

And so let me end where I began. We set out a decade ago to drive data center leadership. We've made tremendous progress on the CPU side. We've made tremendous starting progress on the GPU and the networking side. But with the capabilities and the teams that we have in place, with our strategy and our partnerships, we are highly confident that we can build the best AI solutions for the AI factories of the future out for this foreseeable future.

And when we think about the $1 trillion TAM for AMD, which is -- by the way, that's a silicon TAM just so we're clear. That's not to be compared to some others that might talk about an overall solution TAM. But the $1 trillion silicon TAM, we're very confident in our ability to address it.

We've already grown the AMD data center business from essentially 0 when we began this journey 11 years ago to over $16 billion expected for this year. That's a great growth. But we believe with our portfolio that growth can accelerate. And as Lisa already mentioned earlier, we believe that we have clear line of sight to 60% CAGR over the next 3 to 5 years for the AMD data center business writ large, which generates, you can do the math, over $100 billion in annual AMD data center revenue. It's an incredibly exciting time for our whole team, and we are so, so pleased to be part of this journey. Thank you very much.

Let me now turn it over to Matt, who's going to take us to the break. Thank you, Matt.

Alright. Thank you, everybody. Hopefully, that was an informative session. We are going to take a break. We were running just a hair behind. I think Jack is going to come on and talk about our client and gaming businesses. We are going to do that at 330. So it will be a little bit of a 5-minute delay from what we had put in the agenda, but we wanted to give you guys time to get through the restroom cues and all that fun stuff. So back on here at 3:30. And thank you very much.

[Break]

Please return to your seats, the program is resuming now.

Please welcome back to the stage, Matt Ramsey.

Thank you very much, everybody. Maybe we'll just -- I think there's some people over here still finding their seats, so we'll just give it a couple of seconds. I have no other entertainment to offer, so I apologize.

Anyway, thank you, everybody, for following instructions and getting back to your seats so we can start close to on time. Hopefully, you enjoyed the first few sessions around Lisa, Mark and the data center guys. I think as we move forward here, I'm really excited to welcome to the stage our Head of our Computing and Graphics business, Jack Huynh. He's been running that business for about 2 years, and I think you guys might have noticed the difference in the results of that business in the 2 years he's been running it. So come on stage, my friend. Lots of good stuff to talk about.

Good afternoon. It's great to be here in New York to share AMD's Computing Graphics business is evolving and to share with you where we're headed next. As Matt and Lisa said, I stepped into this role 2 years ago, and we unified the Client and Gaming organization into one business. Today, I'm going to share the momentum and more importantly, the path we're on towards market leadership. And I can't think of a better way to share my excitement for this business than to be here today. We have transformed the business in the past 2 years, and we achieved our market growth through a leadership product portfolio, and we deepened our relationships across the entire ecosystem from OEMs to software developers.

We've also accelerated our time to market and drove R&D synergies across our Client and Gaming organizations. And this strategy delivered 50% year-on-year increase in revenue from $9.6 billion to over $14 billion in just this past year. It all starts with building a great product portfolio. We have built the absolute best desktop portfolio in the industry with our X3D technology. And we also have the top-selling gaming CPUs. And in Mobile, we have the best AI PC portfolio top to bottom. In Enterprise, we are accelerating our commercial share growth. We have the world's best workstation platform with Threadripper.

And early this year, we added Dell as a new enterprise partner with a top to bottom AI PC portfolio, and we have a partner-first mindset. We design and bring the right products at the right time. And we align our road map with our customers' long-term multiyear strategic needs, and we're building a world-class go-to-market operations to further scale the business. And there's no one better to tell our story than our end customers, and the response has been very strong.

Large enterprises are converting to A&D for the very first time and seeing the benefits. Over 50% of Fortune 100 companies have already deployed Ryzen PCs, and we've also expanded our footprint into the public sector. I'm very pleased with the incredible results we have been able to deliver. We have a systematic and strategic approach towards business growth. We have doubled the business in just 2 years. And we've been able to change the economics of client from value to premium, growing our ASPs 50% in the past 2 years as well. At the same time, we've almost doubled our market share while growing our ASPs to a record 28% revenue share.

I love Client, but I'm also a lifelong gamer, and I'm incredibly proud to win consecutive generations of consoles with Microsoft Xbox and Sony PlayStation, which is unprecedented in the industry. We also created a new category of AAA gaming handhelds, enabled the entire catalog of PC games to be accessible on a handheld device, driving a new vector of growth for our gaming business. In consumer graphics, we focus our strategy on delivering the best mainstream GPUs in the marketplace, offering the best performance per dollar. And our graphics architecture is highly scalable. It allows us to address a large range of segments in both Client and Gaming.

And we're working very closely with our software developers to reduce the time of development of all future games, creating massive new world with more immersion, more detail and more exploration, and we're ready for this transition. All this growth we just talked about is still in the early innings of the AI PC acceleration, but we're building towards something even bigger. We're entering a new era where AI is happening everywhere, running directly on the devices that you use every single day. Over the next several years, we expect to see over 1 billion end-user devices powered by AI locally, which is why we're infusing AI into everything we do across Client devices, Gaming and Broader Compute Solutions. And we have the scale to drive the AI PC inflection to the market with the right partners and the right platforms.

We believe that AI will fundamentally augment the value that the PC can deliver. You have thousands interactions with your PC every single day, and AI will be able to understand every personal interaction, bringing automation, deep reasoning, and personal customization to everyone. What AI did to the cloud with large-scale design automation, AI will bring to the endpoint with low latency task and productivity automation. In the education space, AI has the potential to be a personal tutor for every student, an assistant for every teacher. The learning process is highly personalized and AI will be able to adjust for every individual.

Let me show an example of how fast the AI ecosystem is moving. Just 18 months ago, Llama 3 model was introduced, amazing the world with its AI capabilities. At over [indiscernible] billion parameters, there was a mouse that can only be run in the cloud. Fast forward to just a few months ago and similar mouse can now run locally, more efficient, just as performant and just as accurate as the bigger models. This fundamental trend is going to change and continue and will only accelerate the AI PC value.

We have a very aggressive strategy and a very ambitious road map. Our AI PC strategy starts first with building a great PC, a better PC, the perfect PC. And then we add the best AI architecture and capability to it. We have a no-compromise PC strategy, and we're investing ahead of the curve to capitalize on the AI PC value creation to lead the industry through innovation. The future of AI PCs will be built on AMD. And we're aggressively accelerating our Client and Gaming with an AI-first mindset, but it doesn't stop at the endpoint. We see AI expanding beyond the endpoint and the same problem statement we're solving for endpoints also exists at the edge.

The explosion of data by endpoints creates demand for AI at the edge. Besides the need for confidential individual AI assistance, there is a need for confidential enterprise-level AI assistance. This is where Edge AI provide capabilities to deal with the large amounts of data produced by endpoints and extract decision-making ability at the enterprise level.

For every scale of model, there's an optimal hardware to process it. And we released products spanning tens of billions to hundreds of billions of parameters with models. We created a new product category with Ryzen AI Max, a mobile AI workstation with 120 gigabytes of unified memory across the CPU and GPU with full CPU and GPU coherency. It also doubles as a supercharge small form factor AI device supporting up to 120 billion parameter models. We also recently launched our new and first Radeon AI Pro inferencing cards designed for scalable, multi-GPU deployment and it's a very cost-effective solution for large AI models available today.

We also leverage our ROCm Ecosystem from endpoint to cloud to provide a seamless developer experience. And we're deepening our R&D investment to go after this opportunity as we expand our AI portfolio. The opportunity in Client and Gaming has never been greater. We have a very consistent strategy, as Lisa outlined earlier today, building great products, leading the ecosystem, driving operational efficiency. Our focus is to strengthen AMD's market leadership and drive sustained growth. We have a strong leadership road map, a deep ecosystem partnership and a proven track record of execution, and we're confident in achieving revenue growth at more than 3x the market rate while expanding our market share to over 40% in the next 3 to 5 years.

We've built tremendous momentum, and we have a clear path to market leadership. Now we're entering a new era. AI is transforming the PC experience and redefine what compute means across every device in our portfolio. This is not just another product cycle. It's a once-in-a-generation shift towards expanding our opportunity across every segment. Our next chapter is about scaling the Client business, deepening our console advantage and unlocking new growth with AI at the edge. If I can leave you one thought today, it's we are ready to lead the gaming and AI PC era. Thank you.

And with that, let me introduce my colleague, Salil Raje.

Let's talk AMD Embedded. What's happening inside AMD Embedded is one of the biggest transformations at AMD. I bet most of you haven't been thinking much about the AMD Xilinx acquisition since it closed in 2022. But it still stands as the largest and there I say, the best managed acquisitions in semiconductor history. I'm both honored and humbled by the response of taking that historic business and turning it into long-term value for AMD and for the customers we serve. We come a long ways. Let me share with you what we've been up to and where we're headed next.

The scope of AMD Embedded has increased tremendously over the last 3 years. What Xilinx brought to AMD was leadership FPGAs, adaptive SoCs, a whole host of strategic IP, AI engines, high-speed interfaces, but just as important, thousands of loyal embedded customers. AMD delivered high-performance compute IP, CPUs, GPUs, APUs, plus scale, manufacturing depth and advanced packaging technology. We are now leveraging the strengths of both companies to deliver integrated end-to-end solutions for our customers. We are unlocking new vectors of growth and winning designs that neither company could have won alone.

Since the acquisition, we expanded the scope of Embedded significantly. We increased our portfolio to go now from cost-optimized products to high-performance Versal class products. But the Embedded business has been increased beyond FPGAs to now include Embedded x86, where we leverage CPUs, APUs, GPUs, to semi-custom silicon, where we are focused on strategic customers for their high-value engagements to physical AI as cloud intelligence moves to the physical world to the real world. And this is allowing us to tap into $30 billion of TAM. We're continuing to win in our core FPGA business, the adaptive compute business, at the same time, expanding into new spaces, leveraging AMD's IP portfolio and global reach.

Prior to acquisition, our portfolio was narrow. On the adaptive side, we will focus mostly on high-end FPGAs. Our portfolio in the Embedded x86 space was also quite narrow. That meant we had clear gaps in key markets. That limited our reach and constrained our growth. But since the acquisition, we expanded our portfolio significantly. On the adaptive side, we now go from cost-optimized Spartan class products to high-end Versal class products, and that has allowed us to go after markets such as vision, robotics and all the way to aerospace and defense. And this is also enabling us to grow at a rate faster than the baseline FPGA growth.

On the Embedded CPU side, we've taken AMD's CPU business and created a more compelling and competitive product portfolio. And now that's allowing us to go after markets such as industrial, networking and storage. We have launched 40 new products, and now winning designs and building the foundation for semi-custom silicon and physical AI. This is the strongest and broadest and most competitive portfolio that we have ever had and a strategy that is working.

Across FPGAs and Embedded x86, our design momentum is exceptional. We're continuing to double down on adaptive compute, still our crown jewels, now broader, stronger and more competitive than ever. On the Embedded x86 side, we have a highly leveraged purpose-built product portfolio, and that is allowing us to go after newer markets, newer applications, new customers. Our embedded sales team and our channel partner organization have deep relationships with thousands of customers. We are now taking that broad portfolio of products and working with these customers and winning market share across all our markets. And that is synergy that is paying off. Our design win momentum is phenomenal. We won thousands of designs totaling $36 billion plus. And that is execution, that's customer trust, and that's how winning in embedded compute.

As Lisa mentioned and Mark also talked about, AMD Embedded is now in the semi-custom silicon business. Semi-custom silicon business will be one of the largest growth vectors for AMD Embedded and is accelerating fast. We are not just going after every ASIC. We are very focused on a few strategic customers with high-value engagements where these customers are co-developing with us because of our application knowledge, our IP and our execution. We are leveraging the industry's broadest IP portfolio, our FPGAs, our x86 CPUs, ARM SoCs, GPUs, APUs, NPUs, DPUs, RF technology and our advanced packaging technology.

Our financial strength and our scale is allowing our customers to put their entire road maps onto our platform. And that's really the killer combo that is allowing our customers to be anchored to us for their mission-critical multigenerational products. We have secured a significant number of design wins through semi-custom silicon business, $15 billion across automotive, data center, aerospace, defense and wireless. In the years to come, semi-custom silicon business will add meaningful revenue to AMD Embedded, scaling up to almost 1/3 of our business over the long-term horizon. Semi-custom silicon business is one of the clearest examples of how we have transformed from a focused FPGA business to creating a broad compute platform and having entirely no new growth engines.

Now putting it all together across FPGAs, Embedded x86, semi-custom silicon, our design win momentum has never been stronger. Prior to the acquisition, we grew our design wins at single digits. Since the acquisition, we have increased the pace and scale of our design wins and now growing to strong double digits. In 2024, our design wins was about $14 billion. This year, in 2025, we already surpassed that record, and we are now scaling up to $16 billion. This design win momentum is a direct result of our execution of our broader product portfolio, but also, frankly, because we can now put end-to-end solutions in front of our customers. And this is a pipeline that's booked and now positions us well for meaningful revenue acceleration over the next 5 years and beyond.

Speaking of revenue, last 2 years, our business was a bit soft. And the softness had to do with an inventory correction. Most of our customers were draining inventory through their entire supply chain. Now that phase is behind us, and growth is returning to our business. In the future, as part of AMD, our trajectory is completely different. In the near term, we'll grow our revenue at 2x the market rate, scaling up to 3x the market rate over the long-term horizon as semi-custom silicon and physical AI start to ramp. We're also tracking to 70% market share in our core FPGA business as we continue to extend our leadership and start gaining more and more market share in that business.

So this is how AMD Embedded shows up in the P&L. Market share growth in the near term and a clear path from revenue growth, 2x the market rate to 3x the market rate over the long-term horizon. Everything I've heard so far from product portfolio expansion to design win momentum to semi-custom silicon is getting AMD Embedded ready for what's next. And what's next is physical AI. Physical AI will be the biggest transformative vectors for AMD Embedded. Intelligence will move from cloud to the physical world to the real world.

Billions of intelligent systems will be deployed across all industries in applications such as autonomous driving, robotics and drones over the next decade. These intelligent systems will perceive, decide and act instantly. The physical AI market is expected to be $200 billion plus by 2035. AMD is uniquely positioned to win in this market and gain significant market share.

We are already working with many of these customers in these applications. AMD devices are already used to power many of these applications in sensor fusion, in vision, in control logic. We are building upon this foundation for whatever form physical AI takes because we intend to lead it.

Unlike in cloud, which mostly focuses on inference and training, in physical AI, we had to optimize the entire pipeline from perception to decision to actions, all happening in milliseconds. Safety, privacy and real-time performance are important considerations in physical AI. AMD is the only one that can optimize across perception, decision and action, perception being sensor fusion, decision being low latency inference and decision has to do with real-time processing.

For sensor fusion, we have hardened sensor fusion IP for standards-based sensors. We have our adaptive fabric when customization is required. We have for decisions for low latency inference, we have either FPGAs or NPUs. When large models and big tops are required, we have our GPUs. For action, we have x86 CPUs, ARM SoCs for real-time processing under safety constraints. We can mix and match any of these IPs to create purpose-built products for each of the applications. The bottom line is while our competition is mostly focused on inference and providing multichip solutions, AMD is the only one that can optimize across perception decision and action on a single embedded platform.

I have covered a lot today. So if there's one takeaway I want you to have, it's this. AMD Embedded has become a high-margin growth engine for AMD. We used to be a focused FPGA business. We are now a multidimensional powerhouse built upon adaptive compute, embedded x86, semi-custom silicon and physical AI, each a growth engine on its own. Our momentum will make Embedded a defining chapter in AMD's success story. Thank you.

With that, I want to welcome our CFO, Jean Hu. Hi, Jean.

Thank you, Salil. It's great to see everyone. Thank you so much for joining us today. You have just heard from Lisa and the team, our strategy, our technology product portfolio and our focused execution, which is driving tremendous momentum across our entire business. Now it's time to connect the dots to your financial model and in particular, how all of this position us to address very large market opportunity ahead of us and our long-term financial model. Fundamentally, as a management team, we are here to build a compounding business model to drive revenue growth and significant earnings per share expansion to create long-term value.

So when you look at our financial framework for value creation, it has been consistently anchored on 3 core elements. First is about accelerating top line revenue, targeting the most attractive market opportunities, driving technology and product leadership. Second is about delivering compelling profitability, expanding margins and driving operating leverage. And the third, which is most important, as all of you know, is about disciplined capital allocation, not only to fund the growth for the future, but maximize returns for our shareholders and owners. So by laser focusing on these top 3 priorities, this management team has demonstrated a phenomenal track record of execution and value creation. I'll do a quick recap of the last decade how this team has executed.

First, on revenue. If you look at 2016, the company's revenue was only $4 billion. We have been able to drive significant revenue expansion to expected $34 billion with a CAGR of 26%. On profitability, what our team has done is to compound the faster revenue growth pace by expanding gross margin. 2016, the company's gross margin was barely 31%, and we expand gross margin to 45% in 2020, and we expect to deliver gross margin of 54% this year. And of course, the gross profit expand much faster than top line revenue growth at a 34% CAGR.

This team also translated the operating leverage into our overall operating profit increase. If you look at the operating profit in 2016, it was barely $43 million. And this year, we expect to generate over $8 billion operating profit, which is 79% CAGR. So it has been tremendous how this team has executed financially.

On capital allocation, Lisa talked about how we have invested over $100 billion through organic investment and acquisitions and how those investments have positioned us for acceleration of our technology product leadership and also market leadership. This team is very, very focused on return on investment. On organic investment, first, we prioritize R&D investment. So if you look at our operating expense and CapEx, vast majority of investment is on R&D. We have a very rigorous internal resource allocation process each year.

We examine the market opportunities, the assumptions we made on different investment, every project get examined and every investment get examined. The team is not afraid of investing ahead of curve. You heard from Lisa, from Mark, how we establish innovation and long-term leadership. But it's also the case I joined 3 years ago. I'm still impressed and amazed how team's discipline has been if the assumptions we made were wrong and if the project will not generate returns we estimated, they are also not afraid of keeping the project, really allocate the resource to the most important project, which generate the highest return. And over time, Mark talked about how we build the foundation for the future technology leadership and how we shift resources to allocate to data center and AI.

So on the acquisition side, we apply the same discipline. what we have done is not only focus on strategic acquisitions, they have to generate significant returns versus our alternative capital use, like organic investment or do buyback. That is really important for the team. A very good example is the recent acquisition of ZT Systems. We acquired the ZT design team. At the same time, we separated the ZT manufacturing operation and divested that business. So through both acquisition and the divestiture, not only we achieved our strategic objective, like Forrest said, accelerate time to market significantly for our MI450 generation product. More importantly, we generated significant cash for our shareholders through the divestiture. So Lisa mentioned that we have built a tremendous execution machine and the playbook on acquisitions and the integration, so we can really deliver returns for our owners.

When you look at the disciplined capital allocation, the team has done, it really drives significant business transformation and acceleration of our financial momentum. Lisa and Forrest talked about how we really transformed the business to data center, like $2 billion in 2020. Now it's over $16 billion, almost half our business. For CFO, what's really most exciting, it has a higher gross margin. So structurally, we are focusing on the highest growth opportunity and the high gross margin.

And then on financial momentum, we have accelerated our financial momentum. If you look at 2025, we expand revenue growth by 32% based on our estimate for our Q4 guidance. And both Client and Gaming business and the data center contributed to this increase in revenue. On gross profit, we expand gross margin to 54%. And the second half of this year, we're ramping MI350 significantly, but we are able to continue to expand the gross margin. Primarily, it's driven by the newest generation products we're introducing across Ryzen, EPYC and Instinct family. So it's very exciting how we continue to drive the richer product mix across all our business.

On operating income, we are leaning in investing aggressively this year. We continue to develop -- deliver over 33% of operating income. So it's a very powerful business model. We are pleased with our financial performance in 2025 and building on this financial -- strong financial foundation, I want to talk about the opportunities in the future and our future long-term target model. And of course, it all started with the opportunities. I think this team has invested during the last decade to position ourselves to be able to capitalize on the very large opportunities Lisa highlighted.

The data center opportunity, we expect to go from $200 billion this year to over $1 trillion in 2030. So within this really -- given this very large market opportunities, we do see a significant step-up of our growth trajectory. If you look at the last 5 years, we were able to deliver a very impressive 21% top line revenue growth, which is driven by significant data center expansion at 52% and our core business, which include Client and Gaming and Embedded business, including Xilinx growing 10%. Looking ahead, as Lisa and the team talk about this, we expect our data center business to expand more than 60%. Then talk about the significant server TAM expansion driven by AI adoption and how we can target the market share gain to more than 50%. And Forrest, Vamsi talked about the inflection of our data center AI business, which we expect MI450 to start to ramp in second half of 2026. And we see a trajectory to tens of billion dollars of data center AI revenue in 2027. And of course, a trajectory Forrest talked about to be more than 80% CAGR for our data center AI business.

Then coming to our Client and Gaming business and Embedded business, they are vital and diversified element of our business model. Jack talked about the client business, the tremendous momentum of ASP increase and the market share gain will continue to drive that share gain to more than 40% of the market. And Salil just talked about the significant Embedded business design wins and the market recovery, which will outpace the market growth going forward. So when you combine that together, we expect the overall company to expand more than 35% in top line revenue growth.

And if you look at our long-term financial model, we expect top line revenue to grow more than 35% during the next 3 to 5 years. We expect our gross margin to be in the range of 55% to 58%. And we expect our operating margin to be more than 35%, the tax rate of 13% to 15% and the free cash flow margin more than 25%.

So as a Management team, we are very excited about building on this business momentum we have today and execute towards this long-term financial model. Of course, I'm going to double-click on 2 areas I know you are going to ask me about, gross margin and operating leverage. First on gross margin. We do have multiple drivers that we can continue to expand our gross margin. First is scale. It's classic operating leverage. You guys all know. Our team does a great job to leverage scale volume to driving gross margin expansion.

Second, optimization, which is about yield test time improvement design for cost. Those are really important for AMD because we actually have a broad product portfolio, and we target all different end markets and with very high volume. Some of our products last for a long time. So when you make the yield and test time improvement, you actually can make significant impact on gross margin over time. Our operation team has done an excellent job there. I'm very confident they will continue to do so.

Third is mix, which remain to be the primary driver of our gross margin. I'm really excited about several tailwinds we have. First, on the client side, Jack talked about how the ASP increase in moving up the stack has helped us on the revenue share. Frankly, it also helped us with our gross margin expansion. More importantly, we do think we have an upside to continue to expand the client gross margin because right now, it's still very much lower than corporate average, but we do think the opportunity to expand gross margin going forward is going to help the company generate a tremendous bottom line and cash flow.

Secondly, on the mix side, then talk about the server business, continued momentum and the market share gain. More importantly, we are at an inflection point to gain more share on the enterprise market side. Every point of enterprise market share gain is significantly accretive to our overall gross margin. And as Salil talked about the opportunity to outpace the market growth with really high-margin business, especially AMD Xilinx FPGA business is very strong in aerospace, defense, industrial, testing, emulation. We see significant gross margin expansion and growth from those business. So with those really tailwinds, I feel really good about the continued gross margin expansion.

And then let's talk about Data Center AI business. First, Forrest mentioned earlier, we are not selling the level system solutions. Our business model is not going to change. We're selling GPUs, CPUs, sometimes DPUs. So it continues to be the same gross margin structure like we have today. And today, it is true our gross margin is slightly below corporate average because our objective right now is really to scale our business to make sure our customers get a better TCO and also getting more market share. So in a fast-growing market, maximizing gross margin dollars is our #1 priority. But over time, when we continue to add more capabilities each generation, we also can optimize our designs for workload with the scale, we can optimize the manufacturing business and operations, we do see our gross margin to continue to steadily improve over time.

So when you put it all together, there are puts and takes. That's why we are guiding a range of gross margin from 55% to 58%. The mix dynamics certainly determine that, including the pace and the rate of our Data Center AR business ramp. But overall, we feel really good about the trajectory to continue to expand gross margin going forward.

Then operating leverage. 2025 is an important year for us to invest. Given the large opportunities we have, the team is leaning in to accelerate not only the hardware road map, but system software investment. We also did acquisition of ZT System and several software acquisitions to add our capabilities to address this large market opportunities. But we are very committed to drive revenue growth to be faster than operating expense growth. On R&D side, we'll continue to set it as a priority, but we are driving AI adoption across all our engineering team to improve productivity of the company. On SG&A side, we will continue to tightly manage operating expense and also drive automation and AI adoption to drive operating leverage to drop to the bottom line.

So when you look at our long-term model, combined 35% top line revenue growth with 35% -- more than 35% operating margin, it's a very powerful combination, and we do expect to drive our earnings per share to be more than $20 in the forecast period. It's very exciting. And the whole team want to execute on this model to delever just like the track record that this team has showed you in the past.

Now let me switch to capital structure and capital allocation. Our business model generates very strong free cash flow. If you look at 2025, we expect to more than double our free cash flow versus last year, and we have a pristine balance sheet and very strong investment grade. We do view that as a strategic asset. It's tools in our toolbox we can use to continue to drive the company's growth trajectory. And we are also committed to return cash to shareholders. Since 2021, we have returned $8.6 billion cash to our shareholders through buyback.

So our capital allocation principle continue to be the same. First and foremost is investing, especially when you look at the large market opportunities ahead of us, organic investment and acquisitions to really enhance our capability to continue to deliver returns to our shareholders. And from a shareholder return perspective, we'll continue to commit to do the buyback to return cash to shareholders. Of course, offset the employee stock dilution first, but we'll do additional buyback when opportunities arise. Overall, we will continue to maintain our strong balance sheet and financial flexibility to continue to invest for the future.

So in summary, it's a very exciting time to be part of AMD. We are targeting over $1 trillion market opportunity, and we are driving top line revenue growth more than 35% and operating margin more than 35%. And our disciplined capital allocation approach will allow us to continue to compound earnings per share to drive earnings per share to be more than $20 during our forecast period.

With that, I'll invite Lisa come back to give closing remarks.

Okay. Well, that was a lot of information for one afternoon. So I promised to keep the closing remarks very, very short. But maybe I'll just give you a little bit of perspective. I mean, I've been CEO of AMD for a little over 11 years. I'm super proud of the team that we have. And I think the key message is we've always been really clear about what our priorities are. It's about delivering great products and great technologies to the market. It's about having very, very deep and strategic customer relationships. And I can say for sure that there has never been a more exciting time to be at AMD.

If you think about the incredible market opportunity that's out there, if you think about the product portfolio that we have, I think we know how to execute, and we know how to execute at scale, frankly. It's quite different running a $4 billion company to running a $34 billion company. And the only way you do that is with a fantastic team that really knows how to optimize every aspect of the business. And we also have, I think, a history of very strategic and deep partnerships. I think what you're finding right now in the market is the market is favoring those who can partner, those who can bring people together because at the end of the day, I'm a firm believer and there is no one company that has every solution that's needed in the market.

Frankly, what we love is building those partnerships that are one plus one is much greater than three in the sense that we bring to our customers something that they could never get on their own, and they're bringing us insight that we would not get on our own. And that's how we're putting this all together. And the other part is to ensure that for all of you, as our shareholders and our supporters, we're also committed to having a very exciting market return for you. And I think Jean said it best, so we'll follow up with her numbers. We've shown you a lot of numbers today, but perhaps the 3 most important ones are best-in-class growth with greater than 35% revenue CAGR, greater than 35% operating margin and greater than $20 EPS.

So with that, thank you again for spending the last few hours with us. We are super happy to be able to spend some time to talk about the long-term view from AMD. And I think we're going to transition into Q&A at this point. Thank you.

Thank you, everybody. Give us a few minutes to do a little scenery change. While we're waiting for the guys to set up the chairs and for the team to come up, just a couple of housekeeping items and sort of rules of the road on Q&A. No 11-part questions, please. So we're going to -- [ Liz and Prab ] from our IR team are going to be running around with roaming mics, and we'll kind of call on you guys to ask questions. We really appreciate it if you kept it to one question per caller.

And then while the team is sitting down, after we get done with Q&A, there's going to be a cocktail reception over here, I guess, to my left, to your right, all the way around the corner there. There's a bunch of different demos of technologies from across AMD's businesses, including a very interactive fun thing about the Helios Rack. That's a big touchscreen display thing that's actually really cool. So as we wrap up, there's some -- after a day of talks, everybody needs a cocktail plus there's some great demos in the back.

So thank you very much for all of your attention today on us presenting the business. I've been at the company less than a year and have worked with all of these folks for a very long time in my external role, and it's just incredible what the team is executing against. So if you guys don't mind sticking your hands up for questions, maybe we'll start from there.

Since Tim was looking over my shoulder the entire time during the presentation and peer pressuring me, Tim, go ahead.

I just had a quick 2-part question. So sorry. So Lisa, first, on customer concentration. So we see the big deal with OpenAI and there's some overlap with Oracle as well. So how do you think about -- in this time frame, how do you think about customer concentration in the data center GPU business?

And then quickly on component cost increases, Jean, how did you factor that into your model? It seems like you don't buy a lot of these components, but they could really hurt the PC and the server TAM. So I'm just curious your thoughts there.

Sure, Tim. Thanks for the question. And look, the way I would say it is the following. We are super excited with OpenAI and the strategic partnership we announced. I think it sets a foundation given its size and scale, 6 gigawatts over 4 or 5 years, I think, that is a key foundation. But the way you should think about it is our goal is to have really a very broad set of customers. And there are multiple customers that we are engaged with right now at similar scale, multi-gigawatt scale, multigeneration with the notion of -- I mean, MI450 is an extremely competitive solution. I mean, I think I don't want to under -- sort of under describe sort of the big leap there.

We've done a great job with the MI300, 325, 350 family, but MI450 opens up a lot more TAM for us. And I think with the work that we've done on both the hardware and the software platform, we've been deeply engaged with a number of customers all throughout this period. I would say even since the OpenAI announcement, it's really opened up some additional opportunities with the notion of, look, there's -- and I said it, there's an insatiable demand for AI compute, and MI450 is an excellent solution. And I think that's the really anchor point for the type of growth that we're talking about.

So yes, OpenAI is an important foundation, but we will have multiple hyperscalers in the same time frame, multiple customers at gigawatt scale and multiple customers that are looking at multigenerational road maps with us.

I think as we go forward, we even get even more tailored to some of these workloads. So it's like every time we engage at this type of scale, we learn and we get even more tailored to the solutions. I think you saw that in Dan's road map when he talked about what we did in EPYC. I think we're seeing a very similar trajectory with just the depth of the partnerships with the broad customer set.

So on the component cost question, it's a great question. As Lisa mentioned, it's very complex. But Forrest and our operations team have been working very hard to ensure not only we have the supplies component that we know exactly the cost of the component. So we have been very disciplined about that to ensure we can support our customers.

Vivek here in the front.

Vivek Arya from Bank of America Securities. Thank you so much for an informative Analyst Day. So Lisa, I'm trying to understand how you came up with the 80% growth rate forecast for AI. Is it bottoms up? Is it top down? Because if I look at what it mathematically means, it's about $120-ish billion, right, plus/minus by 2030.

So if I take that $1 trillion TAM, it suggests that your share aspirations are somewhere in the mid-teens. But how much of that number is ASICs? How much of that is China? So what is truly -- what I'm really trying to get to is what are really your market share aspirations? And this 80% growth rate, is this built up some from bottoms-up visibility, discussions with customers? Or is it based on a TAM and then a market share aspiration on that?

Yes. Absolutely, Vivek. So many aspects to that question. Let me try to get through it. Look, when we look at TAM as well as revenue projections, I think we do it, first of all, in the near term, it's bottoms up. So bottoms up to look at what exactly are our engagements with customers, especially when we're talking about things like 2026 and 2027.

Frankly, the lead times for wafers, for memory, for components is so long that we need to have that very detailed conversation many quarters out with our customers. And then as we get into the medium term, it's more strategic conversations with customers.

We don't do much from a true tops-down perspective because, again, that's sort of a little bit math. But the way to think about sort of what are our thoughts on the TAM. The TAM, we believe, in this strategic time frame, that TAM includes GPUs, it includes ASICs and accelerators. And our view of that has not really changed. I think we've been consistent. I think there's a place for ASICs in the accelerator TAM.

ASICs tend to be good if your models -- if your algorithms are a bit more stable and if you know exactly what's going to happen next. GPUs, we're innovating at such a fast pace at the annual pace with all the new data formats, with all of the new algorithmic things that are coming out in the models. I mean, GPUs, we believe, will still be the predominant percentage of the TAM. So ASICs, maybe 20% to 25% of the TAM and the rest of that being GPUs.

And then in terms of how do we think about China, I think China is a complex and dynamic situation, certainly as it relates to us being able to service China. As we said in our latest earnings call, we've taken China out of our revenue forecast because it's too hard to call right now. We'd still like to sell to China, no question. But relative to the TAM, it's also a small piece of our TAM for -- again, that's the best visibility that we have at this point in time. And we'll continue to update it as we go forward. Hopefully, I covered your questions.

As someone who spent the majority of his teenage years sitting in the back of the class, Joe, I see you way back there. So I don't want the last row to get left out.

We did not know where Matt was going with that. So we didn't know, you didn't know, but...

Yes, Joe Moore, Morgan Stanley. I guess on the same lines, how do you think about market share 5 years out? I mean, either you're delivering better ROI than NVIDIA is or you're not? Like what's the case for low teens type of share versus much larger share, a much smaller share depending on kind of how you deliver technology-wise?

Yes. Joe, let me comment on that, and that was part of Vivek's question. Look, I think the key thing for us and for the market is no one enters a market to be a very small share, right? So we enter a market thinking that we can be a meaningful portion of the market that we're developing -- we're delivering value. That value is coming in sort of technology capability, like we are doing things in different ways. We're offering products that have really leadership in certain capabilities. And we are also offering overall total cost of ownership. All of those things are important. So certainly, from a market share aspiration, we mean to be a meaningful portion of the market, meaningful double-digit percentage of the market.

Now the market changes, all kinds of things change. But our view is that there is no other silicon TAM that is as exciting as the data center TAM and especially the data center AI TAM. And so we have gone all in, in terms of strategic investments in this area. And I think we have a very, very strong road map. I mean, you heard sort of the very early pieces from Forrest and Vamsi about what we're doing. MI450 was a significant step up. MI500 series is another very significant step up.

And with that, we see opportunity to really differentiate in this market, especially as there's really just a diverse set of workloads, like what you need for the highest-end training may not be the same thing that you need for your best throughput in inference. And actually, I can say for sure, it's probably not the same. And as a result, there are ways that you can tailor products such that you will get best total cost of ownership. And when you're investing CapEx at this scale, I mean it is absolutely worth it for the largest hyperscalers to find the most optimized solutions. And that is, I think, what we see is the conversation is much more workload-specific today than do you have a GPU that works.

Sticking with the back row theme. Stacy, go ahead.

Stacy Rasgon at Bernstein. So you've talked about tens of billions of dollars of AI revenue in '27. You do, I don't know, $6.5 billion or so this year. If I just took that 80%, that would put you at around $20 billion or $21 billion in '27, which I guess is tens. Now maybe it's possible that the growth rate should be faster in the early years than the later years just given the base is smaller. I think the Street is modeling, I don't know, $29 billion to $30 billion in '27. Like how do you feel about your, I guess, that medium-term trajectory relative to where those kind of current expectations are?

Yes. I think, Stacy, the way I would say it is, since we're giving a 3- to 5-year TAM and the outer years have a little bit less visibility than the near-term years, we would expect the near-term years to grow faster than 80%, so greater than greater than 80%. And I think we are comfortable with tens of billions of dollars being in the ZIP code of, let's call it, where some of the thoughts are overall.

Maybe do you want to add to that?

I think you covered it. I think when we look at the current consensus for 2025, it's roughly in line, and we do expect, like Lisa said, to grow faster.

Josh, over here on the right, my right.

Thanks for hosting such an informative day. I think, Lisa, in your prepared remarks today and on the earnings call last week, you called out multiple gigawatt scale projects that are in development. What hurdles need to be cleared to convert those, both on your side and the customer side? And any context or scope you're able to provide on what those look like compared to the OpenAI engagement you were able to give us a lot of details on publicly?

Yes. So Josh, thanks for the question. I think the best way to say it is we have very deep relationships with the hyperscalers and all of the hyperscalers and many of the top AI native companies. You can imagine that everyone is super interested in MI450. And we expect that MI450 will be deployed at very significant scale.

In terms of multiple gigawatt customers, my comment is really around just the scale of forecasts that we're getting from customers on their near-term needs. So there is a desire for a significant amount of compute. We are working with the supply chain today to make sure that we have the broad ability to support all the compute that's required and just to make sure that we have that out there.

I think we have built a supply chain for the type of growth rates that we're talking about. And I think the customer momentum is very strong. So much of the work right now is continuing to work with Vamsi on some of the software aspects of it, but it's really in execution mode for MI450 as we get those fully ramped up and validated, we would expect to convert a number of those opportunities.

All right. I'm going to shift gears and go to the front row. Liz, I can do it. Don't worry.

Aaron Rakers with Wells Fargo. I want to shift gears a little bit. The embedded presentation, there were several references to semi-custom opportunities, particularly around the data center side. So I'm curious of what exactly is that? Are you going to participate in some, if I call it, XPUs or XPU attach opportunities? When do you see those maybe materializing? And Forrest, real quickly, I'm curious on silicon photonics, Enosemi, I think, was an acquisition you made. Where does that stand and kind of the strategy from a rack-scale perspective as well?

Yes. Sure, Aaron. Maybe I'll start, and then I'll let Forrest talk about optics. Look, semi-custom has been in our strategic sort of tool chest for the last number of years. I really believe in this sort of business model. It's actually not a product, it's actually a capability to take our IP and really tailor it for specific applications.

I think Salil has done a particularly good job at taking the -- all of the semi-custom components and our technology IP to some of his top embedded customers, and they've seen a lot of value in that. And so you saw in their aerospace and defense customers, automotive, a number of communications sockets as well where there isn't something that's very easy to pick off the shelf, but we have all the pieces for them to put it together.

To your specific conversation about data center, we have won several data center semi-custom opportunities. They tend to be more -- not the compute unit itself, but some of the attach around the entire system, including some networking components and some other specific components.

I think if you were to fast forward and talk about how we could imagine semi-custom evolving, I think you can imagine semi-custom as built off of our GPU compute capability. So the idea that -- the beauty of chiplets is you really can customize pretty easily, right? You could take our entire foundation and say, hey, let me take this compute die off sort of the AMD standard product and put on a customer-specific standard product. And I don't know what you call that. Do you call that a GPU or do you call that an XPU or do you call that a GPU plus.

But at the end of the day, what we're trying to do is provide workload-specific optimization for the highest volume sockets. As I said to Vivek's question, I still believe that standard product GPU will be the largest driver of growth just given the rate and pace of innovation that's happening there. But there may be opportunities for us to do customized GPUs as well if there are specific workloads or use cases. I think it just broadens our overall capability in the data center space and really across the computing space.

Maybe, Forrest, on the optics.

Yes. And Aaron, I think on the optics side, for the last 20 years, I'd say that silicon photonics and optical interconnect within the Rack has been a technology that's always 3 to 5 years out. And it's been that way for the last 20 years. And the ingenuity of the engineers in getting copper-based solutions to continue to scale has been incredible and better than expected. And so that's pushed that horizon of the age of optics out continuously.

I think as Mark mentioned earlier, we were approaching the point though where it's pretty clear that just the age of optics is almost upon us and that we see that time horizon that's indefinitely been out there is collapsed. And so in the -- we believe in the '27, '28, '29 time frame, you're going to see a transition, first in the large-scale rack global systems for scale-up fabrics and then over time, other places as well. You're going to see a transition to optics. And it just from an I/O density point of view, from a bandwidth point of view, from a power point of view, it just makes all the sense in the world.

So we're certainly getting fit to fight. We've -- you mentioned Enosemi, that was a small acquisition to augment our already existing optics team. But as Mark said, we feel in '27 and beyond, it's an era where optics and SerDes will continue to coexist for a short period of time, but it's all going optics long term.

I don't know if you wanted to add anything to that, Mark?

No, well said.

I don't know what it is about sell side either in the front row or the back row, but Chris Rolland in the back there.

Chris Rolland, Susquehanna. Mine is for Jean. If I'm doing the numbers right, it seems like maybe OpEx growth is, call it, mid-20s versus revenue growth at 35%. I guess, first of all, should we front-end load that? Could it even be higher than 25%? And then secondly, the Street's dialed in for high teens for next year, mid- to high teens. Does that mean like the direction of travel is higher? And should we adjust our models?

Yes, Chris, thanks for the question. The way to think about it is 2025 is really a year we're investing -- leaning in, investing significantly more. So if you look at the OpEx increase this year, it's actually pretty high. But going into next year and going forward, we do believe we can drive revenue growth to be higher than OpEx growth.

So year-over-year, I would not suggest you to front-load all. It's literally from a modeling perspective, what's the best way to model it, you can just model it that way. So I don't have a clear -- like path, right? We are doing our [ 2026 ] annual operating plan right now. So it is, as I said, a rigorous planning process to figure out where we're going to invest. But one thing I can tell you is revenue growth next year will be higher than OpEx growth.

Right. I think I can't see, Brett, but I know he's behind that very large pole. So we'll go back there. Thank you.

Yes. I'm hiding here in the background. It's Brett Simpson at Arete, and thanks again for hosting an informative day. Lisa, my question is about supply in the industry. And you put out a large CAGR for data center and the TAM in 2030, it translates to a huge amount of fabs that need to be built for the industry.

So my question is, I guess, DRAM looks like it's going to get really tight into next year. Foundry at leading edge is extremely tight as well. So when you -- can you maybe talk about what AMD is doing with their key suppliers in terms of getting aligned with how you see the growth trajectory in AI?

Yes. Thanks for the question, Brett. So look, we have been actively planning the supply chain all along. So certainly for the last few years, and going into next year and the following years. I think the fact of the matter is I think we have a lot of experience in sort of being first on some of these things. We're very early in the advanced nodes. So planning is very important. We're very early on high-bandwidth memory. So planning is super important on that.

And the best thing I can say is, for sure, the supply environment is getting tighter, but I can say that we feel very confident that we have the overall supply chain capability to support the growth model that we're talking about. And the key about the industry in general is we've generally been able to really get supply to ramp when it's clear where the demand is. And as everyone is putting out their markers right now in terms of what they think the overall demand is, we're working very closely with our top suppliers to actually triangulate all of that and ensure that we have really our fair share of the growth that's needed to support all of this.

Ben Reitzes from Melius. I appreciate it. I wanted to readdress the first question with regard to OpenAI and say something that's on everybody's mind. There's obviously this worry about OpenAI that's just with the customer concentration, the CapEx concentration in the industry, people are particularly worried about it now. I'm sure this isn't news to you. You just put out these really great forecasts that are awesome and congrats. Did anything happen that makes you just more confident about it, maybe more line of sight on the 6 gigawatts? Anything you can say to comfort a crew that just on the customer concentration question that gives you more conviction in these great forecasts, considering they can be $20 billion plus in revenue in a given year?

So I think the best way to think about it, Ben, is the following. First of all, I will say OpenAI is definitely one of the most aggressive when it comes to their compute forecast. So that is definitely true. I think the way we've structured our engagement is it's a very disciplined engagement. I mean, as much as we like the headline numbers of 6 gigawatts over the next 4 or 5 years, it's a very disciplined engagement on what do you need each year and where are you going to get it from.

And the expectation is that we've already said the first gigawatt will start in the second half of '26 and will go into '27. It is a very disciplined way of saying, hey, we expect it to be installed in these data centers at this point in time, this is when you have to have your supply ready. And that's the only way that we make these types of things happen. This was really what was so important because it is such an impactful deal is to ensure that we put all of those measures in place in dealing with OpenAI as a customer.

That being the case, I will say that we have a broad base of customers that we serve in the data center and include the comment or the question earlier, about customer concentration in the Data Center AI business. We expect to have multiple similar-sized customers in the strategic time frame, in the MI450 time frame, that is how much interest there is in MI450. So that means that we're dimensioning the supply chain to supply multiple gigawatt scale customers, and we're working with our partners to do that.

So I think the best way to say it is we are quite disciplined in how we plan these things. I mean, it's significant scale and significant volume, but we're also quite comfortable that we know how to do it.

All right. We're going to do a little ping-ponging here. Antoine, I think you're way over on this side, and then we're going to go to Will, who's way over on that side. Just for fun.

Actually, maybe if I want to ask one on the Edge AI opportunity. So you've been mentioning there's been a lot of chatter around a lot of compute from content delivery networks, Edge telco. I'm just wondering what use cases AMD is seeing at the Edge?

Maybe Jack and Salil, do you want to come on up and say a couple of things?

We're already seeing use cases in the, call it, 500 parameter model. If you think about the latency from the cloud to the endpoint, we're seeing opportunities in hospitals, education, factories. They just want the workloads be much lower -- closer to the endpoint, right? And we just launched our first Radeon AI PRO Card just actually a few weeks ago, and demand is actually looking pretty good, right? So think about it as we have a broad range of capability across tens of billions to hundreds of billions of parameter models, and we're seeing workloads as very diversified, and we want to cover every possible workload out there.

Yes. So we do distinguish between Edge AI and Physical AI. So I'll talk a little bit about Physical AI. And Physical AI is where AI has been used in the real world, basically things like autonomous driving, right, and drones, robotics and things like that. But it's really cutting across all industries, I would say, right? Almost every application, every algorithm is being rethought these days. And people are thinking about how do we apply AI instead of the traditional algorithms, right?

Things -- you -- probably in the past, you've heard of OpenCV and [ regional gardens ], none of them exist anymore, right? We have all the AI algorithms, right? So in some ways, right, every device, every application in the physical world will have some AI infused in them, which is why the TAM is super big in the silicon TAM for Physical AI.

All right. Will?

It's Will Stein from Truist Securities. Lisa, thank you for hosting such an informative day with these very nice aspirational TAMs and margins and other earnings growth, wonderful. And I appreciate your prior comments about the diligence in building up this forecast. But there's 2 constraints that people ask me about a lot that seem a little bit further away from AMD's control than typical -- than what we typically see.

One is your customers' ability to fund all this and the other is their ability to source power. How much time are you spending on those things? And I think we'd really appreciate some clarification as to why you believe that this is going to be able to be funded and powered? And should we expect other sort of off-the-run financing deals like we saw with OpenAI?

So a couple of different questions there. So to answer your question on how much time do we spend on, let's call it, the things beyond delivering Helios and the GPUs and things like that. We spend a lot of time on those things, right? We have to. I mean that is part of how we get comfort and confidence in the size of market and what happens.

So certainly, on power, we're spending quite a bit of time looking at the power road map, not just for our customers, but just around the world, where do we see sources of power. We're thinking -- we're talking a lot about the U.S., but you should think that there are significant ramps in power outside of the U.S., especially when you look at places like the Middle East, Southeast Asia, a bunch of other places. So we're looking at power road maps there. We are working actively with each of our large customers on their power road map to ensure that we know when their data centers are ready. So that's how we're matching up the forecast.

And then the second point, as it relates to how do we think about financing. Certainly, financing is a key piece of it. Maybe if we just maybe put OpenAI aside for 1 minute because I think that might be a bit of a topical conversation. All of the other large hyperscalers who are talking about raising their forecasts are extremely well funded. I mean, their balance sheets are really strong.

And the fact that they are choosing to invest more in AI should be a good indicator to the audience that they see value in it. I can tell you the same customers that I might have talked to 12 months ago or 18 months ago, maybe didn't say that they were going -- they weren't forecasting that their CapEx would just keep going up. What they've seen is they've seen real value in their business and in their sort of overall strategy to do that. So I think we should feel confident that the hyperscalers can afford to invest the type of CapEx that we're talking about, and it's because they expect to see the return on the other end.

And now coming back to OpenAI and addressing that. There's no question that OpenAI is leading the market in terms of forecasting what they want in terms of compute. I think, again, not to speak for them, but if you take a look at some of their numbers and the number of the growth of their user base, the growth of their ARR and just the amount of users that still don't have as much compute as you want, I wouldn't bet against that. I really wouldn't.

What we must do as sort of good stewards of the company are ensure that everything is cause and effect. And I think that's why when you look at the structure of our deal with OpenAI, it's a very much aligned incentive structure in the sense as they see visibility for, obviously, funding and installing all of that GPU capability or AI capability, we also plan together with them.

And the reason that we are so forward leaning on this is it is great for us in terms of just the amount of learning that we get from engaging at gigawatt scale with a customer that's on the bleeding edge of foundational models. I mean that is -- I would say, Vamsi's team has had a lot of torture, but also a lot of fun in the process.

So I think we're doing this in a very structured way. We absolutely think about all of the aspects that you're talking about. And when we look at all of the above, we say this is a very unique moment in AI. And we shouldn't be shortsighted in thinking about, hey, are you going to see returns in a couple of quarters? Or are people going to be interested in financing it? If the AI usage grows as much as we expect, I think there's going to be plenty of financing for all of the return capabilities that are there.

Blayne?

Blayne Curtis at Jefferies. Maybe an AI adjacent question. I wanted to ask on general-purpose servers. You showed the market accelerating. I didn't know how literally to take the 60% overall data center and then 80% AI, it would get you a pretty substantial double-digit server number. So maybe how are you thinking about the market and then your growth on top of it?

Yes. Do you want to take that, Dan? You can take it.

Blayne, thanks for the question. So on server, look, we -- I showed the TAM for AI. The general purpose TAM is also double digit, right? So we believe that there's a lot of growth potential there. And with our road map, we believe that we could definitely far outgrow the market as we have been and continue to gain share. I'm not sure if that got to your...

Maybe let me add something to that, Blayne. If what you're asking is what are we thinking about server TAM. Yes. So server TAM, I think Dan showed something like high teens TAM growth rate. The way to think about that is, though, again, back to the question of how do we get to those numbers. In the near term, we have very clear forecasts from our customers for 2026, 2027. The uplift in sort of cloud demand has actually surprised us. And we saw the first aspects of it.

A couple of quarters ago, it was maybe one customer who we thought, well, maybe they're building ahead or they're refreshing. And now we've seen it at multiple hyperscalers. And when we ask underneath it what's happening. It is, as Dan described. There are multiple vectors. As you have more AI inferencing that's being done, you need more general purpose compute. As you have agents really start kicking in, it's like having 1,000 more people, like they have to be computing on something and they need general purpose compute to compute there.

This feels like a real durable trend. And I think that's really nice because there was this debate. I mean, frankly, for the last couple of years, we showed the TAM was relatively flat, right? There wasn't a lot going on. There was a little bit of -- I think people were holding off on some of their refreshes as they were trying to figure out how much to invest in AI. And now they've realized that, yes, I have to invest in AI, but yes, I have to invest in general purpose compute as well. And it plays really well into our road map just given the strength of our overall road map.

Liz, maybe you could come to Gary and then Lou will be over here.

Gary Mobley at Loop Capital. I had a question about ARM-based compute, not as a competitive threat, but as an opportunity. Now I understand that you use the N-Series cores in Pensando and whatever you do in Xilinx as well. But what about licensing or creating [ V-Series ] ARM-based processors for head-end compute or even leaking into the custom compute opportunity?

Do you want to talk a little bit about where we use ARM and I can add.

Yes. So we certainly -- so ARM is a partner for us in a number of different products. We use -- as you mentioned, we use some N cores in the Pensando technology. We use some of the semi-custom products as well. And we expect that to continue. I mean, they've got a strong road map, and there's some applications for which that's a good fit.

Certainly, beyond that, if there's semi-custom opportunities where customers particularly want ARM as a compute engine, we are more than happy to do so. And we've invested in having our foundational infrastructure be able to support either x86 or ARM compute elements. So the Infinity Fabric that Mark talks about all the time, that plumbing is set up to give us diversity. So it really will depend on customer choice.

But I would be remiss if I didn't also say that for Dan's road map, we think that we are certainly competing for TCO and performance advantage, not just with Intel, but also with ARM, and we really like his road map. We think that it offers outstanding performance, outstanding TCO value at multiple different points. It's one of the great things about having both the performance and frequency optimized cores as well as the cost and area optimized cores coming from the same technology tree. It gives us very broad coverage of pretty much every space that's pertinent in the Data Center. So I really like Dan's road map against all covers.

Okay. Lou Miscioscia, Daiwa Capital Markets America. So the comments before about Edge AIP seems very interesting with potential great growth. I realize writing applications on the enterprise or the consumer side takes a lot of time. But when do you think we'll actually see an inflection point there? And also given -- if I saw the charts correctly, you talked about growth and share gain, but you didn't give a TAM. So anything that you could fill in there would be very helpful.

Jack, do you want to take that?

The client TAM, we're projecting low to mid-single digit in the next 3 to 5 years. Then on Edge AI, beauty of it is we have the same ROCm software stack that goes from Instinct to Edge portfolio to make it very seamless for our developers. And we're just getting started. So we have our first Radeon AI Pro that we just launched a few weeks ago, and we built up this road map to be AI first versus gaming first. And you'll see future products that we have announced yet in upcoming updates.

C.J., I see you -- and you were kind enough to wear a tie. Well done, you.

I figure if I'm sitting in the back row, I should probably have dressed nice, but thank you for today. Appreciate it. C.J. Muse with Cantor Fitzgerald. So clarification and then a question. Clarification, on the $1 trillion TAM and your target revenues, are you including HBM in there? I would be curious how you're thinking about HBM within your gross margins?

And then to my question. Curious around your partnership with OpenAI and their Triton software stack, where try to kind of make heterogeneous hardware environment. I would love to know how that is informing your business with them and how perhaps that's enabling you with other customers?

Sure, C.J. So clarification on the $1 trillion TAM. So that is a silicon TAM. So that includes GPUs, that includes the HBMs that go with the GPUs. That includes CPUs, and that includes actually the networking part of the market that we service. So let's call it scale-up networking. It does include scale-out and switches.

And the way we think about HBM is on the software we're doing the primary design, we should get a healthy margin. I mean that's where all of our investments are going. For the things where we're attaching, I think we are adding value in the overall, let's call it, package, but we're not expecting the same margin on HBM. And I think that's fair because if you think about just where the value is coming from, we want to ensure that in the end, we have a good overall TCO to our customer set. So that's the way I would state how we're thinking about it.

And then in terms of OpenAI and the heterogeneous environment, maybe Vamsi, I'll let you talk a little bit about the software environment.

Yes. So maybe first, I would say that the work that we have done with Triton is very aligned with our overall strategy to leverage the rising abstractions, right? So Triton is actually a higher-level programming abstraction. So it's more productive and easier to compile to hardware. So that's why we collaborated with them. It's going well, the Triton enablement.

And what it does is when a new workload comes along, it actually takes down the amount of time to get on to hardware. So if you have, let's say, a model that's stitched with, let's say, 100 kernels, maybe there's a few kernels that will remain to be written with a little bit more sort of lower-level programming, but a majority of them can actually be compiled with Triton and it gets us to market faster. So that's the advantage.

And also, the work is not specific to OpenAI. Triton actually is used quite heavily in the industry when a lot of the new day 0 support comes along, actually, it gets enabled because Triton's back-end support for AMD GPUs is becoming more and more adopted.

All right. Our handy-dandy little clock here, the yellow light just went on. So I think we have time for maybe 2 more questions. Chris, since you're right here in front of me. I will walk over here.

Since I'm in the front row. So Chris Caso from Wolfe Research. Jean, a question for you with regard to the gross margins. And one of the things you said is that in the near term, your priority was to maximize gross profit dollars. So how do I reconcile that with the longer-term guidance, 55% to 58%? What's the time frame for that 55% to 58%? And should we assume, therefore, that the gross margins are diluted somewhat in the early stages of the MI450 ramp?

Yes. The first thing I would say is if you look at our gross margin, we have been expanding our gross margin in 2025 each quarter. We are ramping MI350 actually steeply in the second half of 2025. So overall, from our gross margin profile perspective, we absolutely want to make sure we continue to improve gross margin with all the tailwinds I talked about it.

On the mix perspective, as you can imagine, it will really depend on the pace and the ramp of the Data Center AI business, right? So we said that we're going to start to ramp in second half 2026, then into 2027, it's going to ramp significantly. Our other business gross margin will continue to go up. That mix change in that range, absolutely, if the volume is really high, it could be closer to the 55% range. And relatively speaking, if the mix is dynamic differently, we could be on the other high end of the gross margin, but that is in the forecast period between 3 to 5 years.

In the near term though, does -- is there an early dilution...

The way to think about near term is we are ramping MI350, right? And we'll continue to ramp in the first half of next year. So our gross margin should be quite consistent with what you are seeing right now. We guided Q4 at 54.5%. I think the second half when we start to ramp MI450, that is when it's really going to depend on the volume, right, the pace of the ramp and the volume. Right now, actually, I don't know yet.

All right. So Mr. Lipacis, no pressure. You're the last question before we go to cocktails.

Great. Thank you for the excellent presentations, very informative. So Lisa, at the -- so I was actually at the Analyst Day where you guys made the observation that you rounded to 0% share in server CPUs and then made the case that you were going to take share. And I think you upsided everybody's expectations, certainly mine.

Can you -- it seems like there's a lot of similarities in how you prosecuted the server CPU market and how you're going after the GPU market in AI. And I was hoping that you could just lay out that playbook, the important elements of the playbook that you used for CPUs and how you're laying that out right now for the GPU.

And then on the other side, the competitive environment, you have a different competitor. And I'm hoping that you could just help us understand what's different on the competitive front from a challenge standpoint.

Sure. Well, I'll start, and then I'll let Forrest or Mark or Vamsi add if they have things to add. So look, on the CPU side, I think we always started with the notion that this is about technology. And it is when you have sort of disruptions in technology, you can actually make a really big difference if you make the right technology bets.

And I think what you saw a little bit in what each of Mark, Dan and Forrest presented today is it was actually very, very systematic. I mean, I think when we started with our plan, we said, look, it's going to take us 3 generations to be competitive. However, we know exactly which step we're going to lay out as we went from Naples to Rome to Milan. Post that, it became -- we were at scale now. And so when you're at scale, you can actually fan out and address more of the total addressable market. And that's when we went to multiple cores and multiple segment optimization.

And now I think we're in a different place. And the different place is when you're now the incumbent. So you have to imagine like we don't take any share for granted, okay? So every generation, we have to prove that we are better and better by a lot. But when you're in the incumbent position, you just have such a different seat at the table, and that's where we are today in server CPUs, especially in the cloud.

I think we're planning long-term road maps, multiple generations. And frankly, I can't say that we get it right all the time, but our customers are very willing to tell us where we can adjust and where we should adjust so that we can be a very significant piece of that business. And so yes, we're fortunate that at the same time that the market is inflecting, we have such a strong product portfolio.

And if you translate that into the GPU side, it is similar and different in cases. From the standpoint where it's similar, it is a foundational is all about the technology. We know that. And we've also had a very deliberate path of going from MI250 to MI300 to MI350. We chose not to do Rack Scale solutions this year because we thought that, that would be hard, and we wanted to prioritize time to market of our new data formats and new solutions. But we chose to set ourselves up so that MI450, we had all of the pieces. That's why we did the ZT acquisition. That's why we did the Pensando acquisition. That's why he's been hiring like crazy on the software side. And then as we go to MI500, there's another set of significant innovations that are coming on board.

So I think it is similar in the -- from the standpoint that it's very foundational technology. Where it is slightly different is the size of the market and the speed of the market in AI is different than what we saw in the general purpose CPU market. And I actually think that plays in our favor because when you have a market that's moving that fast and when there's so much opportunity to disrupt and innovate and when one day you're thinking about training systems and the next day, you're thinking about fine-tuning and inferencing systems and you need to deploy quickly, it favors differing solutions.

So I think we've made tremendous progress. We give all of our competition lots and lots of credit because that's who we are. We earn every socket, but we feel really good about our trajectory. And I'll let you guys add. You can see we're passionate about this topic as a management team. So...

We're super passionate. I'll just make a quick comment and connect it back with you what you heard Jean in talking about our disciplined capital allocation. We run a very, very disciplined process. When you look at that pyramid I showed of the value of all the building blocks leading down to our product, pillars, that capital allocation is scrubbed many times over to understand what value it's providing.

So your analogy to the server road map. That expanding road map was deliberations year after year several years in advance for the arc of R&D that you need to decide that differentiation by listening to our customers, build it in, deliver it. Same thing with the GPU. So we have created a very disciplined process as we make those decisions across every business unit with R&D closing back with Lisa and Jean, and we're going to continue that going forward.

It's not often that I publicly disagree with Lisa, but let me publicly disagree with her on one point. She said it's all about technology. And I would only quibble with the word all. It is -- that is the table stakes that allows you to play at the table. But the other critical element that we learned during the server journey and that I think is pertinent here as well is that the other element because you're talking about a market and applications that are core to our customers, they're very important to our customers.

The other element is customer trust. And building that trust where they understand that they can rely on AMD, that they can rely on our technology that we're going to execute for them, it's extremely important. And we've built that in a very deliberate way on the server side. And I think actually, we're doing the same thing here as well, phased approach to building with our lead customers, trials with MI250 or 300, expanding the usage and things like the validation that the implicit validation from large customers embracing MI450 are very important in reinforcing that lead customers have trust in AMD to provide the core of their AI solutions, and that also is an incredible validation for the rest of the market.

So sorry to disagree with you a little bit, Lisa.

That's what a great team is for.

Well said, Forrest. Just a couple of things for me. Thank you to all the executives for coming together here today. You can feel free to give them a round of applause. But I think it's just as important to talk about the people that didn't come up on stage as a whole another part of the executive team that wasn't presenting today.

There was a hell of a lot of work done by Liz and her IR team, by Phil and the communications team, by the events team, by everybody at AMD. We're trying really hard to represent the 25,000 engineers and do them proud. And it takes a lot of work to put on an event like this. All my days on the sell side coming to these things, I had no idea how much work it was. I found that out. And I just want all those people to be recognized as well.

So Lisa, the whole team, thank you very much, and let's have a cocktail.

Greetings, and welcome to the AMD Third Quarter 2025 Conference Call. [Operator Instructions] As a reminder, this conference call is being recorded. It is now my pleasure to introduce to you Matt Ramsay, VP, Financial Strategy and Investor Relations. Thank you, Matt. You may begin.

Thank you, and welcome to AMD Third Quarter 2025 Financial Results Conference Call. By now, you should have had the opportunity to review a copy of our earnings press release and the accompanying slides. If you have not had the opportunity to review these materials, they can be found on the Investor Relations page of amd.com.

We will refer primarily to non-GAAP financial measures during today's call. The full non-GAAP to GAAP reconciliations are available in today's press release and the slides posted on our website.

Participants in today's conference call are Dr. Lisa Su, our Chair and CEO; and Jean Hu, our Executive Vice President, CFO and Treasurer. This is a live call and will be replayed via webcast on our website.

Before we begin the call, I would like to note that Dr. Lisa Su, along with members of AMD's executive team, will present our long-term financial strategy at our Financial Analyst Day next Tuesday, November 11 in New York. Dr. Lisa Su will present at the UBS Global Technology and AI Conference on Wednesday, December 3. And finally, Jean Hu will present at the 23rd Annual Barclays Global Technology Conference on Wednesday, December 10.

Today's discussion contains forward-looking statements based on our current beliefs, assumptions and expectations, speak only as of today and as such, involve risks and uncertainties that could cause results to deliver -- to differ materially from our current expectations. Please refer to the cautionary statement in our press release for more information on these factors that could cause actual results to differ materially.

And with that, I will hand the call over to Lisa.

Thank you, Matt, and good afternoon to all those listening today. We delivered an outstanding quarter with record revenue and profitability, reflecting broad-based demand across our data center AI, server and PC businesses.

Revenue grew 36% year-over-year to $9.2 billion. Net income rose 31%, and free cash flow more than tripled led by record EPYC, Ryzen and Instinct processor sales. Our record third quarter performance marks a clear step-up in our growth trajectory as the combination of our expanding compute franchise and rapidly scaling data center AI business drives significant revenue and earnings growth.

Turning to our segments. Data Center segment revenue increased 22% year-over-year to a record $4.3 billion led by the ramp of our Instinct MI350 Series GPUs and server share gains. Server CPU revenue reached an all-time high as adoption of 5th Gen EPYC Turin processors accelerated rapidly, accounting for nearly half of overall EPYC revenue in the quarter. Sales of our prior generation EPYC processors were also very robust in the quarter, reflecting their strong competitive positioning across a wide range of workloads.

In cloud, we had record sales as hyperscalers expanded EPYC CPU deployments to power both their own first-party services and public cloud offerings. Hyperscalers launched more than 160 EPYC-powered instances in the quarter, including new Turin offerings from Google, Microsoft Azure, Alibaba and others that deliver unmatched performance and price performance across a wide range of workloads. There are now more than 1,350 public EPYC cloud instances available globally, a nearly 50% increase from a year ago. Adoption of EPYC in the cloud by large businesses more than tripled year-over-year as our on-prem share gains are driving increased demand from enterprise customers for AMD cloud instances to support hybrid compute.

We expect cloud demand to remain very strong as hyperscalers are significantly increasing their general purpose compute capacity as they scale their AI workloads. Many customers are now planning substantially larger CPU build-outs over the coming quarters to support increased demands from AI, serving as a powerful new catalyst for our server business.

Turning to enterprise adoption. EPYC server sell-through increased sharply year-over-year and sequentially, reflecting accelerating enterprise adoption. More than 170 5th Gen EPYC platforms are in market from HPE, Dell, Lenovo, Super Micro and others, our broadest portfolio to date with solutions optimized for virtually every enterprise workload.

We closed large new wins in the quarter with leading Fortune 500 technology, telecom, financial services, retail, streaming, social and automotive companies as we expand our footprint across major verticals. The performance and TCO advantages of our EPYC portfolio, combined with our increased go-to-market investments and the expanded breadth of offerings from the leading server and solutions providers, position us well for continued enterprise share gains.

Looking ahead, we remain on track to launch our next-generation 2-nanometer Venice processors in 2026. Venice silicon is in the labs and performing very well, delivering substantial gains in performance, efficiency and compute density. Customer pull and engagement for Venice are the strongest we have seen, reflecting our competitive positioning and the growing demand for more data center compute. Multiple cloud OEM partners have already brought their first Venice platforms online, setting the stage for broad solution availability and cloud deployments at launch.

Turning to data center AI, our Instinct GPU business continues to accelerate. Revenue grew year-over-year driven by the sharp ramp of MI350 Series GPU sales and broader MI MI300 Series deployments. Multiple MI350 Series deployments are underway with large cloud and AI providers with additional large-scale rollouts on track to ramp over the coming quarters.

Oracle became the first hyperscaler to publicly offer MI355X instances, delivering significantly higher performance for real-time inference and multimodal training workloads on OCI's zettascale supercluster. Neocloud providers Crusoe, DigitalOcean, TensorWave, Vultr and others also began ramping availability of their MI350 Series public cloud offerings in the quarter.

MI300 Series GPU deployments with AI developers also broadened in the quarter. IBM and Zyphra will train multiple generations of future multimodal models on a large-scale MI300X cluster. And Cohere is now using MI300X at OCI to train its Command family of models.

For inference, a number of new partners, including Character.AI and Luma AI, are now running production workloads on MI300 Series, demonstrating the performance and TCO advantages of our architecture for real-time AI applications.

We also made significant progress on the software front in the quarter. We launched ROCm 7, our most advanced and feature-rich release to date, delivering up to 4.6x higher inference and 3x higher training performance compared to ROCm 6. ROCm 7 also introduces seamless distributed inference, enhanced code portability across hardware and new enterprise tools that simplify the deployment and management of Instinct solutions. Importantly, our open software strategy is resonating with developers. Hugging Face, vLLM, SGLang and others contributed directly to ROCm 7 as we make ROCm the open platform for AI development at scale.

Looking ahead, our data center AI business is entering its next phase of growth with customer momentum building rapidly ahead of the launch of our next-gen MI400 Series accelerators and Helios rack scale solutions in 2026. The MI400 Series combines a new compute engine with industry-leading memory capacity and advanced networking capabilities to deliver a major leap in performance for the most demanding AI training and inference workloads. The MI400 Series brings together our silicon, software and systems expertise to power Helios, our rack scale AI platform designed to redefine performance and efficiency at data center scale.

Helios integrates our Instinct MI400 Series GPUs, Venice EPYC CPUs and Pensando NICs in a double-wide rack solution optimized for the performance, power, cooling and serviceability required for the next generation of AI infrastructure and supports Meta's new open rack wide standard. Development of both our MI400 Series GPUs and Helios rack is progressing rapidly, supported by deep technical engagements across a growing set of hyperscalers, AI companies and OEM and ODM partners to enable large-scale deployments next year.

The ZT Systems team we acquired last year is playing a critical role in Helios development, leveraging their decades of experience building infrastructure for the world's largest cloud providers to ensure customers can deploy and scale Helios quickly within their environments. In addition, last week, we completed the sale of the ZT manufacturing business to Sanmina and entered a strategic partnership that makes them our lead manufacturing partner for Helios. This collaboration will accelerate large customer deployments of our rack scale AI solutions.

On the customer front, we announced a comprehensive multiyear agreement with OpenAI to deploy 6 gigawatts of Instinct GPUs with the first gigawatt of MI450 Series accelerators scheduled to start coming online in the second half of 2026. The partnership establishes AMD as a core compute provider for OpenAI and underscores the strength of our hardware, software and full stack solution strategy. Moving forward, AMD and OpenAI will work even more closely on future hardware, software, networking and system-level road maps and technologies.

OpenAI's decision to use AMD Instinct platforms for its most sophisticated and complex AI workloads sends a clear signal that our Instinct GPUs and ROCm open software stack deliver the performance and TCO required for the most demanding deployments. We expect this partnership will significantly accelerate our data center AI business with the potential to generate well over $100 billion in revenue over the next few years.

Oracle announced they will also be a lead launch partner for the MI450 Series, deploying tens of thousands of MI450 GPUs across Oracle Cloud Infrastructure beginning in 2026 and expanding through 2027 and beyond.

Our Instinct platforms are also gaining traction with sovereign AI and national supercomputing programs. In the UAE, Cisco and G42 will deploy a large-scale AI cluster powered by Instinct MI350X GPUs to support the nation's most advanced AI workloads. In the U.S., we are partnering with the Department of Energy and Oak Ridge National Labs to build Lux AI, the first AI factory dedicated to scientific discovery together with our industrial partners, OCI and HPE. Powered by our Instinct MI350 series GPUs, EPYC CPUs and Pensando networking, Lux AI will provide a secure open platform for large-scale training and distributed inference when it comes online in early 2026.

The U.S. Department of Energy also selected our upcoming MI430X GPUs and EPYC Venice CPUs to power Discovery, the next flagship supercomputer at Oak Ridge designed to set the standard for AI-driven scientific computing and extend U.S. high-performance computing leadership. Our MI430X GPUs are designed specifically to power nation-scale AI and supercomputing programs, extending our leadership, powering the world's most powerful computers to enable the next generation of scientific breakthroughs.

In summary, our AI business is entering a new phase of growth and is on a clear trajectory towards tens of billions in annual revenue in 2027, driven by our leadership rack scale solutions, expanding customer adoption and an increasing number of large-scale global deployments. I look forward to providing more details on our data center AI growth plans at our Financial Analyst Day next week.

In Client and Gaming, segment revenue increased 73% year-over-year to $4 billion. Our PC processor business is performing exceptionally well, with record quarterly sales as the strong demand environment and breadth of our leadership Ryzen portfolio accelerates growth.

Desktop CPU sales reached an all-time high with record channel sell-in and sellout led by robust demand for our Ryzen 9000 processors, which deliver unmatched performance across gaming, productivity and content creation applications. OEM sell-through of Ryzen-powered notebooks also increased sharply in the quarter, reflecting sustained end customer pull for premium gaming and commercial AMD PCs. Commercial momentum accelerated in the quarter with Ryzen PC sell-through up more than 30% year-over-year, as enterprise adoption grew sharply, driven by large wins with Fortune 500 companies across health care, financial services, manufacturing, automotive and pharmaceuticals. Looking ahead, we see significant opportunity to continue growing our client business faster than the overall PC market based on the strength of our Ryzen portfolio, broader platform coverage and expanded go-to-market investments.

In gaming, revenue increased 181% year-over-year to $1.3 billion. Semi-custom revenue increased as Sony and Microsoft prepared for the upcoming holiday sales period. In gaming graphics, revenue and channel sell-out grew significantly, driven by the performance per dollar leadership of our Radeon 9000 family. FSR 4, our machine learning upscaling technology that boosts frame rates and creates more immersive visuals saw rapid adoption this quarter with the number of supported games doubling since launch to more than 85.

Turning to our Embedded segment. Revenue decreased 8% year-over-year to $857 million. Sequentially, revenue and sell-through increased as the demand environment strengthened across multiple markets, led by test and emulation, aerospace and defense, and industrial, vision and health care.

We expanded our Embedded product portfolio with new solutions that extend our leadership across adaptive and x86 computing. We began shipping industry-leading Versal Prime Series Gen 2 adaptive SoCs to lead customers, delivered our first Versal RF development platform to support several next-generation design wins and introduced the Ryzen Embedded 9000 series with industry-leading performance per watt and latency for robotics, edge computing and smart factory applications.

The design momentum remains very strong across our Embedded portfolio. We are on track for a second straight year of record design wins already totaling more than $14 billion year-to-date, reflecting the growing adoption of our leadership products across a broad range of markets and expanding set of applications.

In summary, our record third quarter results and strong fourth quarter outlook reflect the significant momentum building across our business, driven by sustained product leadership and disciplined execution. Our data center AI, server and PC businesses are each entering periods of strong growth, led by an expanding TAM, accelerating adoption of our Instinct platforms and EPYC and Ryzen CPU share gains.

The demand for compute has never been greater as every major breakthrough in business, science and society now relies on access to more powerful, efficient and intelligent computing. These trends are driving unprecedented growth opportunities for AMD. I look forward to sharing more on our strategy, road maps and long-range financial targets at our Financial Analyst Meeting next week.

Now I'll turn the call over to Jean to provide additional color on our third quarter results. Jean?

Thank you, Lisa, and good afternoon, everyone. I'll start with a review of our financial results and then provide our outlook for the fourth quarter of fiscal 2025.

We are pleased with our strong third quarter financial results. We delivered record revenue of $9.2 billion, up 36% year-over-year, exceeding the high end of our guidance, reflecting strong momentum across our business. Our third quarter results do not include any revenue from shipments of the MI308 GPU products to China. Revenue increased 20% sequentially, driven by strong growth in the Data Center, and Client and Gaming segment and modest growth in the Embedded segment.

Gross margin was 54%, up 40 basis points year-over-year, primarily driven by product mix. Operating expenses were approximately $2.8 billion, an increase of 42% year-over-year as we continue to invest aggressively in R&D to capitalize on significant AI opportunities and go-to-market activities for revenue growth. Operating income was $2.2 billion, representing a 24% operating margin. Taxes, interest expense and other totaled $273 million. For the third quarter of 2025, diluted earnings per share were $1.20 compared to $0.92 a year ago, an increase of 30% year-over-year.

Now turning to our reportable segments, starting with Data Center. Data Center segment revenue was a record of $4.3 billion, up 22% year-over-year, primarily driven by the strong demand for 5th Generation EPYC processors and Instinct MI350 Series GPUs. On a sequential basis, Data Center revenue increased 34%, primarily driven by strong ramp of our AMD Instinct MI350 Series GPUs.

The Data Center segment operating income was $1.1 billion or 25% of revenue compared to $1 billion a year ago or 29% of revenue, driven by higher revenue partially offset by higher R&D investment to capitalize on significant AI opportunities.

Client and Gaming segment revenue was a record of $4 billion, up 73% year-over-year and 12% sequentially, driven by strong demand for the latest generation of client and graphic processors and stronger sales of console gaming products. In the client business, revenue was a record $2.8 billion, up 46% year-over-year and 10% sequentially, driven by record sales of our Ryzen processors and richer product mix. Gaming revenue rose to $1.3 billion, up 181% year-over-year and 16% sequentially, reflecting higher semi-custom revenue and strong demand for our Radeon GPUs. Client and Gaming segment operating income was $867 million or 21% of revenue compared to $288 million or 12% a year ago, driven by higher revenue partially offset by increase in go-to-market investment to support our revenue growth.

Embedded segment revenue was $857 million, down 8% year-over-year. Embedded was up 4% sequentially as we saw certain end market demand strengthen. Embedded segment operating income was $283 million or 33% of revenue compared to $372 million or 40% a year ago. The decline in operating income was primarily due to lower revenue and end market mix.

Before I review the balance sheet and the cash flow, as a reminder, we closed the sale of ZT System manufacturing business to Sanmina last week. The third quarter financial results of the ZT manufacturing business are reported separately in our financial statements as discontinued operations and are excluded from our non-GAAP financials.

Turning to the balance sheet and cash flow. During the quarter, we generated $1.8 billion in cash from operating activities of continuing operations, and the free cash flow was a record of $1.5 billion. We returned $89 million to shareholders through share repurchases resulting in $1.3 billion in share repurchases for the first 3 quarters of 2025. Exiting the quarter, we have $9.4 billion authorization remaining under our share repurchase program. At the end of the quarter, cash, cash equivalent and short-term investments were $7.2 billion. Our total debt was $3.2 billion.

Now turning to our fourth quarter 2025 outlook. Please note that our fourth quarter outlook does not include any revenue from AMD Instinct MI308 shipment to China. For the fourth quarter of 2025, we expect revenue to be approximately $9.6 billion, plus or minus $300 million. The midpoint of our guidance represents approximately 25% year-over-year revenue growth driven by strong double-digit growth in our Data Center, and Client and Gaming segment and a return to growth in our Embedded segment.

Sequentially, we expect revenue to grow by approximately 4% driven by double-digit growth in the Data Center segment with strong growth in server and continued ramp of our MI350 Series GPUs, a decline in our Client and Gaming segment with client revenue increasing and the gaming revenue down strong double digits, and double-digit growth in our Embedded segment. In addition, we expect fourth quarter non-GAAP gross margin to be approximately 54.5%, and we expect non-GAAP operating expenses to be approximately $2.8 billion. We expect net interest and other expenses to be a gain of approximately $37 million. We expect our non-GAAP effective tax rate to be 13%, and diluted share count is expected to be approximately 1.65 billion shares.

In closing, we executed very well, delivering record revenue for the first 3 quarters of the year. The strategic investments we are making position us well to capitalize on expanding AI opportunities across all our end markets, driving sustainable long-term revenue growth and earnings expansion for compelling shareholder value creation.

With that, I'll turn it back to Matt for the Q&A session.

Thank you very much, Jean. John, we can go ahead and poll the audience for questions now. Thank you.

[Operator Instructions] And the first question comes from the line of Vivek Arya with Bank of America Securities.

I had a near-term and a medium-term question. For the near term, please, I was hoping if you could give us some sense of the CPU-GPU mix in Q3 and Q4. And just tactically, how are you managing this transition from your MI355 towards MI400 in the second half of next year? Can you continue to grow in the first half of next year from these Q4 levels? Or should we expect some kind of pause or digestion before customers get onboard the MI400 Series?

Sure, Vivek. Thanks for the question. So a couple of comments. We had a very strong Q3 for the Data Center business. I think we saw a strong outperformance in both the server as well as the data center AI business and a reminder that, that was without any MI308 sales.

The MI355 has ramped really nicely. We expected a sharp ramp into the third quarter, and that proceeded well. And as I mentioned, we've also seen some strengthening of the server CPU sales and not just, let's call it, near term, but we're seeing our customers are giving us some visibility in the next few quarters that they see elevated demand, which is positive.

Going into the fourth quarter, again, a strong Data Center performance, up double digits sequentially and up in both server and data center AI, again, on the strength of those businesses. And to your question, I mean, we're not guiding into 2026 yet obviously. But given what we see today, we see a very good demand environment into 2026, so we would expect that MI355 continue to ramp in the first half of '26. And then, as we mentioned, MI450 Series comes online in the second half of 2026, and we would expect a sharper ramp as we go into the second half of 2026 of our data center AI business.

And for my follow-up, there is some industry debate, Lisa, about OpenAI's ability to kind of simultaneously engage with all 3 merchants and the ASIC suppliers just given the constraints around power and CapEx and their existing kind of CSP partners and so forth. So how are you thinking about that? Like what is your level of visibility in the initial engagement and then more importantly, how it kind of broadens out into '27? Is there a way that one can model what the allocation would be? Or just how should we think about the level of visibility in this very important customer?

Yes, absolutely, Vivek. Look, we're very -- obviously, very excited about our relationship with OpenAI. It's a very significant relationship. Think about it as it's a pretty unique time for AI right now. There's just so much compute demand across all of the workloads. I think in our work with OpenAI, we are planning multiple quarters out, ensuring that the power is available, that the supply chain is available.

The key point is the first gigawatt, we will start deploying in the second half of '26, and that work is well underway. And we continue -- just given where lead times are and things like that, we are planning very closely with OpenAI as well as the CSP partners to ensure that we're all prepared with Helios so that we can deploy the technology as we stated. So I think, overall, we're working very closely together. I think we have good visibility into the MI450 ramp, and things are progressing very well.

And the next question comes from the line of Thomas O'Malley with Barclays.

Congrats on the good results. I had a first question on Helios. Obviously, with the announcement at OCP, customer interaction has to be growing. Could you talk about, into next year, what your view is on discrete sales versus system sales? When do you see that crossover kind of happening? And just what initial responses have been from customers after getting a better look at it at the shelf?

Yes, sure. Tom, thanks for the question. There's a lot of excitement around MI450 and Helios. I think the OCP reception was phenomenal. We had numerous customers and frankly, bringing their engineering teams to understand more about the system, more about how it's built. There's always been some discussion about just how complex these rack scale systems are, and they certainly are, and we are very proud of the Helios design. I think it has all the features, functions, reliability, performance, power performance that you would expect.

I think the interest in MI450 and Helios has just expanded over the last number of weeks, certainly with some of the announcements that we've made with OpenAI and OCI as well as the OCP show with Meta. I think, overall, from our perspective, I think things are going really well in both the development as well as the customer engagements there. So in terms of rack scale solutions, we would expect that the early customers for MI450 will really be around the rack scale solutions. We will have other form factors as well for the MI450 Series, but there's a lot of interest in the full rack scale solution.

Super helpful. And then as my follow-up, it's a broader question as well and similar to kind of what Vivek asked. But if you look at the power requirements that are out there for some of the early announcements into next year, they're pretty substantial. And then you also have component issues that you're seeing across interconnected memory. Just from your perspective as an industry leader, where do you think that the constraint will be? Will it come first with components not being available? Or do you think that both data center footprint in terms of infrastructure and/or power is the gating factor to some of these deployments into next year just as we really see some larger numbers start to get deployed.