Advanced Micro Devices, Inc. (AMD) Earnings Call Transcript & Summary

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.