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

Yes. Look, this is a hot topic. And I think I would start with saying that this is more of a distributed systems architecture problem as opposed to a simple linear problem, right? If you think about the world of ChatGPT from November 22 to probably into last year, very linear, right? It's a SaaS-based data center, you have your servers for web serving, you've got your application servers. You've got your database storage, you've got cashing and then you've got sort of this GPU server, right, which everyone understands the GPU server, right? You know the ratios everyone can calculate that very easily. And that was very linear, prompt response, right? That's what it was built for. Well, with the genetic, as you all know, it's an entirely continuous flow. It's a completely different compute paradigm. It's 24/7 churning whether -- within a sandbox spanning numbers of different agents. So if you just think of the picture, I tried to just draw for you, if you think of your traditional servers here and your big GPU servers here, you kind of open it up and you pull in a whole new class of compute which is for agentic control plane, API calls, database queries, tool execution. And that is pure CPU based. So that clearly will do with the GPU server. So the GPU services grow also. But if you think about those general purpose servers, those get uplifted too, because more and more calls to those -- so you're seeing an uplift in a whole new class plus the traditional general purpose. And we're just seeing that dramatically grow, right? And at our fab in November, I said that look, there's multiple areas of growth for the CPU, we called it, but we called it too low, right? So we've upped it now. And I think the growth we're seeing across both the enterprise and the cloud is very, very exciting. And then lastly, what I would say is with Venice, we are hitting on the 3 main focus areas for CPU, right? You've got your GPU server that everyone knows and loves in terms of started out 1 to 4, a CPU to -- then you've got this genic sandbox CPU where with Venice, with our High Corco56-core device, that is -- if you think about Agentic, it is really threads per watt. -- with the right level of per core performance. If you think about the head node, it's really about IPC and high frequency, driving and keeping the GPUs busy. And then the general purpose servers, we've been very, very strong there for many years and we're going to continue. So when you think about it, we feel like with -- not only with turn today leadership, Venus, as we launched it already and as it comes online here through the back half of this year, we are extremely well positioned to capture this growth. But I'd say 1 last thing. If you're trying to find a number to plug into a model, it's very, very hard because there are so many things. If you just think of a gigawatt of power, right, and then you factor in your PUE and you come up with your IT power. It's all about the addition of the CPUs, again, the host node, you know, we all know that's easy calculation. -- but it all depends on what you're trying to run. It's really workload dependent. And that's why it's so hard to plug a number in, but that's why we tried to capture sort of, hey, this is the growth we see. And when we show it for Agentic, it is also pulling in the uplift in those general purpose servers that I talked about. So I don't know if I confuse you more or not, but just trying to give you the picture of what we're seeing.

Dan, maybe I'd just add 1 thing. I mean we did take a $60 billion TAM out to 2030 and up that now to $120 billion and then $220 billion and the companies -- I know what lease is expecting of you is for your business to be over 50% of that TAM as we grow. And it will be we can do a relatively small number of chiplets and put them together in configurations that could be a significant number of SKUs and a full coverage of the platform. So I mean, what you guys can do the math on more than 50% of the $220 billion. I mean, it is -- I've been following and now part of AMD server business for a very, very long time. And to talk about building a $100 billion server business is pretty exciting. But the -- that's what we see coming is a significant growth of Genetic sandbox CPUs for which we have very large core count multi-threaded parts, strong growth of head node CPUs where we have really high frequency focused high bandwidth, high single thread performance parts and then the broad range of the server market, 1 of the things that stuck out to me seeing the results of Dan's businesses in the second quarter, I mean, it seems like forever ago, we talked about the second quarter, but even the enterprise part of the server business grew more than 70%. That -- the industry has not seen those type of growth rates in enterprise server basically ever. So we're very excited about all parts of the server business and the breadth of SKUs and the breadth of platforms as we roll out Venice and then move into the Florence generation is something that we're really excited about.

Service CP market, as I said, talkabout, it's also becoming increasingly competitive, even as it's growing. So what advantages do you think the x86 ecosystem continues to provide for the enterprise, specifically -- and how do you think about the durability of x86 in the hyperscale environment, especially for some of these internal workloads where customers are developing their own silicon. .

Yes. This is a common question, right? So I mean, first and foremost, we always talk about this, right? This is not an instruction set architecture problem or concern, right? There's no fundamental differences in the ISA between x86 and in arm. It's really about delivering to different optimization points, right? It's per watt per dollar, ultimately. And we know that if we continue to drive along the 3 swim lanes that we just talked about, and optimize for that performance per watt. We're in a very, very good position. And if you think about from an ecosystem taper, if you go back to that picture, I tried to draw at my hands, all those general purpose servers that I talked about x86-based today. Lots of software built for x86. So the ecosystem is built around X86. So all that growth comes on x86. Now if you if you look at sort of the hyperscalers, each 1 of them are doing some form of their own. And what we see is if we continue to drive just what I talked about, which is the highest throughput and core density per watt, -- and then we hit these other points. We feel extremely good about the design in that we have right now across all of the major cloud vendors in the world. across -- from a genetic standpoint, at 256 core from a high frequency standpoint at 96 core and then just across other SKUs for high-performance computing. And even though -- I'll just give you a good example, like recently, Amazon came out with RDS, which is their database service, which is a first-party property, right, that we would classify. It's on turn. And the reason why is performance. So we just know that, yes, would they -- there is a focus for them to try and get their first-party properties on their homegrown, but it doesn't fit for everything. And again, it's -- even when you go high density, it's that perf per core sweet spot and that optimization point on the VF curve that we very -- we pay close attention to. So we really feel like where we are today with coming out with Venice will turn today with Venus coming out as we speak and ramping. And then I just -- I was looking at -- we had a review earlier this week on even in terms of what our engineering teams are targeting. So I feel very, very good about where we are in terms of delivering the optimization points, that's the key, right? It's really optimizing for the different workload and the deployment model.

I think, Dan, I agree. I mean from my perspective, it's not watching the teams internally that the investor focus tends to be much more around instruction set. And it is important for the enterprise pieces of the server market, whether that's on-prem deployment or in cloud deployment. But the economics of rolling out the server market to sort of unprecedented scale that we talked about with the TAM, it's about building the best server parts. -- period. Never mind the instruction set. And I think that's what we -- from a scale and supply chain point of view from a I think you said optimization points and the number of SKUs that we can roll out, the number of platforms that we can roll out, the significant amount of optimization you can do for different places in the road map. I feel really good about where we are. And it's -- and you can -- it's not just what we think about the market. We can see the demand pull from customers for different optimization points. And so when we think about, okay, this is where the demand pull is and these are conversations that are multi-generation in nature. I think we feel really good about where this.

I would just final point on that is for Venice, and I'm pretty sure Lisa talked about this at our last earnings. But with each generation, we built builds on the next, right? And you get more and more of the ecosystem coming along with you as you go. And we've been very, very focused on that. But with Venice, it's the broadest true launch that we've had in terms of OEMs, ODMs, cloud vendors, the ISVs on day 0 support, we're just very -- the demand is very, very strong. Due to the 3 swim lanes that I talked about, I think our customers in the ecosystem are seeing that 1 SKU doesn't solve every problem, right? And that's kind of what we're seeing from a merchant ARM standpoint, it's really just sort of singular SKUs or 1 or 2 SKUs. So we're pretty excited because it really is in a -- we're in a very good spot from a market opportunity standpoint and our product portfolio leadership across really, I would argue, 3 generation straight.

Excellent. One thing that's striking me over the past couple of years, we've kind of changed the parlance of how we talk about this market. We're not talking about server counts or county accelerators. We're talking about counting gigawatts of capacity. And every single presentation at this conference has done that. So maybe as you think about these multi-gigawatt AI deployments, how should investors be thinking about CPU content per gigawatt.

Maybe I'll start. We -- I think the focus that we have at AMD broadly in our data center business is to make sure that we provide very compelling tokens per dollar and TCO for our GPU business, and we're right in the throws of ramping and launching Helios and 45 -- and you'll see us be a very large partner to some of the leading model companies in the world to run their infants were close. Separately, Jim, regardless of whether the inference runs on our GPUs or NVIDIA GPUs or TPUs or whatever XPU. I mean, Dan can expand on this, but I think what we're focused on in the server business is to make sure that AMD's Venice portfolio and going forward are the they're the differentiated and right place for the industry to run agent code. And so we haven't been super specific about what that ratio is in terms of gigawatts of deployment because it does look different depending on what customer it is, but -- we want to grow a very large AI business, and I think Dan's business is positioned to be a significant majority of the industry running agents to power a genetic AI. And so we haven't been super specific on the gigawatt comments in terms of CPU.

Really simple. It depends. Because the challenges is -- so take a gigawatt, you do again, do your PUE, you've got this IT and then you've got to break it down where, okay, I've got clusters of GPUs over here training. I've got clusters here doing inference, then I've got to support it with a general purpose and then the agents and it just really depends on what you're trying to accomplish with that gigawatt. And it's very hard to just say, "Oh, here's a fixed ratio. I would say that it's growing, right? Like if you think about it today, we're saying one-to-one sort of ratio and it's going to continue to grow. But it's just very hard to pinpoint plug this into model and you'll get what you're looking for. It's very highly dependent on what the end customer is trying to accomplish.

I spend a lot of time plugging numbers in the model, so Okay. We're almost out of time, but let me last -- leave you with the last question for you. If we are we cut a lot of ground. If you think about your position in AI, compute, data infrastructure, a center infrastructure, et cetera, first up and here on stage again in 5 years, and we look back, where do you think the 1 thing or 2 things that investors are going to be most surprised about in terms of the performance of the company. .

Dan, do you want to start off?

Look, I think -- maybe I'll start with maybe what people may be missing about us, right? And it's what I talked about earlier is we have fully transitioned from a very, very good silicon provider across multiple products to -- and we are transitioning now to full rack scale. And our software has come even over the last 6 months, the gains we've seen we're becoming more of a software company and a systems company today than we were even 6 months ago. So I would just say that, that will be -- I think if you look forward 12 to 24 months, I think it will become very clear how we have made that transition quickly. And we've driven a software stack that is truly focused on time to value for our customers. I think that's where I would -- I'd leave it in terms of what you'll see over the next few years.

I mean from my perspective, we're -- the goal is to -- I mean, Dan started the conversation this way, Jim, where we want to provide the industry that consumes high-performance computing with the right type of computing for the right type of workload. And I think that, that will serve us well across the breadth of our markets. And we're right now 1 of the more exciting times that the industry has seen and more exciting times for the company, we've talked about much more than doubling our data center business next year and driving gross margin dollars very significantly faster than expenses, right? So we're at that inflection point. And I think it's important for the investor community to understand that Lisa and the whole team is '21, but we were doing a meeting in a room at the conference just an hour before we came on stage here and Dan was on an execution meeting with Lisa and the team, right? So it's like the team is focused on making sure that we have a cadence of execution at the company and despite all the excitement there the focus remains on making sure that we execute. And if we do that, then I think investors will be really pleased with where things end up. But we -- it's not about driving -- for us, it's about driving outcomes for customers and that will translate into outcomes for the investment community, not the other way around. So we're just going to put our heads down and execute because it's a super exciting time, but -- as we wrap up here, the little blinking light is on, but thank you all for spending time with us. And thank you, Jim and the team at Goldman for hosting us. We really appreciate it.

Matt, thanks for being here.

Appreciate it. Thank you.