AMD AI Part I 20250612

Now you gus know us well.At AMD, we're really focused on pushing the boundaries of high performance and adaptive computing to help solve some of the world's most important challenges. And frankly, computing has never been more important.I'm always increadibly proud to say that billions of people use AMD technology every day. Whether you're talking about services like Microsoft Office 365 or Facebook or Zoom or Netflix or Uber or Saleforce or SAP, and many more, you're running on AMD infrasturcture.

And in AI, the biggest cloud and AI companies are using Instinct to power their latest models and new production workloads, and there's a ton og new innovation that's going on with the new AI startups.For example, life sciences company 310. AI use MI300X o train a model that turns simple test propts into novel poteins to really help accelerate drug discovery.Our versatile Aladaptive SOCs are being used to build more efficient 5G networks and improve drive motive safe.

And Ryzen is bringing AI to PCs, enabling more intuitive,responsive, and more powerful experiences.Now since ChatGPT launched a few years ago,the pace of AI innovation has been unlike anything I've seen in may career.And in 2025, it's only gone faster.We've seen the emergence of more powerful reasoning models, the rise of agents. really growing momentum in real world use cases that are actually starting massive and it's clear that we're entering the next chapter of AI.Now,training is always going to be the foundation to develop the models, but what has really changed is the demand for inerence has grown significantly, driven by more capable models and new cases that are increasing AI usage.We're also seeing an explosion of models.So of cource you have, you know, the new frontier ,odels from folks like OpenAI and Google, but you also have open models from Meta and DeepSeek and many other. And we're also seeing a surge in new specifia;ized models that are built from everthing from healthcare to finance to coding to scientific research. And when you look over the next few years, one of the things that we see is we expect hundreds of thousands and eventually millions of purposebulit modles, each tuned for specific taks, industry, or use cases.

And as AI dose more complex tasks, like reasoning, you expect agents to become more capable. It drives significantly more compute, which frankly is great for all of us.Now let me talk a little bit about agentic AI. You know, agentic AI actually represents a new class of user.One thing that is always on, constantly accessing data, looking at applications,looking at systems to really make decisions and really work autonomous. Thye need high performance GPUs to generate insights in real time, but that's really only part of the story.What we're seeing now is as agentic AI activity increases, all of those agents are now also spawning a lot of traditional compute going to high performance CPUs.And just think about it, what we're actually seeing is we're adding the equivalent of billions of new virtual users to the gopute infrasture. All of these agents are here help us, and that requires lots of GPUs and lots of CPUs working together in an open ecosystems.

Ok,let's talk about the martket.

When we were here last year, we sai that we expected the data AI accelator TAM to grow more than 60% annually to $500 billion in 2028. And frankly, for many of the analysts and folks, you know, at the time, that seemed like a really number. People were like, "Do you really think it can be that big, Lisa". And I said," Well, you know, that's what we're seeing'.Uh, and what I can tell you, based on ererything that we see today, that number is going to be even higher, exceeding 500 billion in 2028. And most importantly, we always believed that inference was actually be the driver of AI going froward and we can now see that inference inflection point. With all the new use cases and reasoning modle, we now expect that inference is gonna grow more than 80% a year for the next few years, realy becoming the largest driver of AI compute.And we expect that high performance Gonna be the wast majority of that market because they provide the flexibility and programmability that you need as models are continuing to evolve and really algorithms are moving so fast,you want that programmability in your compute infrastructure.

Now, the other thing that we see is AI is also moving beyond the data center, from intelligence systems at the edge to PC experiences, and we expect to see AI deployed in evry single device.Now, to enable all of this, you don't have any one architecture that is the right answer.So i like to say there's really no one size that fits all.What you need is the right compute for each use case and that's exactly what we're focus on.

Our stategy is reallly focused on three key principles.

First, we're delivering a broad portfolio of compute engines so customers can match the right compute to the right modle and the right use.

Second, we're investing heavily in an open developer first ecosystem, and you're gonna hear us talk about open a lot today. We're really supporting ever major framework, every library, every model to bring the insher in open standards so that everone can contribute to AI innovation.

And third, we're delivering full stack solution. We're buliding, we're forging partnerships. You're gonna hear from some of our partners about our ecosystem today to really put all of these elements together.

From a protfolio，we offer the most complete suite of computing elements end to end for this visiom. That includes CPUs, GPUs, FPGAs,and adaptive SOCs.No matter where AI runs or how much compute you need, AMD has the right solution.There are a lot of developer in this audience and online, so this is really talking to you. Thank your for being here. Thank you for coming today. And we believe an open ecosystem is actually essential to the future of AI. AMD is the only company commited to openness across hardware, software, and solutions. And when you jusy take a look back, some of the most important breakthroughs in tech actually started out closed, if you think about things like, um, early networking protocols, Unix operating systems, and even mobile platforms.But the history of our industry shows us that time and time again, innovation truly talks off.Linus surpassed Unix as a data center operating system of choice when global collaboration was unlocked. Android's open platform helped scale mobile computing to billons of users.And in each case, openness deilvered more competition, faster innovation , and eventually a better outcome for users.And that's why for us at AMD, and frankly for us as an industry, openness shouldn't be just a buzzword.It's actually critical to how we accelerate to the scale, adoption, and impact of AI over the coming years.

Now we also recognize that thses AI systems are getting super complicated and full stack solutions are really critical.So to deliver full stack AI solutions, we've significantly expected our investments over the last few years, both organically and through strategic acquisitions and investments, We're very happy to say that we recently closed our acquisition of ZT, giving us new capabilities in rack and data center scale design that are becoming extremely useful for what we're doing next.

And we've also strengthed our software stack, acs like nod.AI,Mipsology, Silo.ai, and in the last several weeks, we announced adding the Breum and the Lamini teams to AMD.

And we're also investing broadly in the AI ecosystem.over the last year,we've actually done more than 25 strategic investments that have been a great way for us to build new relationships and also support the AI software and hardware leaders of tomorrow.

So let's talk a little bit about customers.We have tremendous mementum in the data center.Since launching in 2017,EPYC med the data center.Today ,EPYC is trusted by the world's largest cloud providers and businesses to run their most important workloads. EPYC powers everthing from hyperscale services to enterprise data centers, supporting the most important workloads with leaders in financial services, healthcare, media, and manufacturing. And our momentum is just accelerating. We excited the last quarter with a record 40% market share, and we believe with AI and high performance compute, there's a lot more room for us to grow.

In AI, MI250X and MI300A enabled the exascale supercomputing era.I'm very happy to say actually this week, there was a new top 500 list that was released, and AMD powers the two fastest, supercomputers in the world. So that pretty cool. and Thank you.

And with MI300X and 325, we've extended that leadership to GenAI with large scale internal and cloud deployments at Microsoft, Meta, Oracle ,and many others. And i'm happy to say we'venew Instinct customers in the last nine months.Today, seven of the top 10 model builders and AI companies are using Instinct in their data centers. Leaders like OpenAI, Meta, xAI, and Tesla.Innovators like Cohere, Luma, and Essential, and many, many more. You're gonna hear from several of them. They're our guests here today, and they'll tell you a little bit about how we work together.Now as powerful as our hardware is , it's truly the software that enables their full potential. And i hear from lots of you as developers on waht we can do better in software.

I can say that I hear you and our ROCm software stack continues to make just incredible progress. We're really focused on broadening the coverage for AI models, accelerating the pace of our releases, and really setting a north star of adeveloper first mentailty with ROCm. When you o our engineers, what I say is, it is all about the developer experience. It's all about what you guys say, and this is our guiding principle. So you're gonna hear a lot about that from Vad then for a ton of developer content to just show you how you can really use, AMD and ROCm.

Now to give you some perspective about what it's like to use AMD, i'd like to bring out my first guest. One of the newest partners who is running Instinct in their production environment is xAI, and here to share more, please welcome Xiao Sun.

-We have a decent audience today. what do you think?

-Ah, that's a great audience.

-Xiao, we are super excited about the work that we are doing with x. You know, you guys are really at the forefront of developing stateoftheart AI models. Um ,you're going super fast.Can you shaer a little bit about what your team does, you know, how are you managing all this.

-Sure, xAI, we have a very samll team, and then we're moving very fast, and, we're following first principle. Basically, you konw, we are advanced like a Grok family models. And then for, maximum truthseeking. And to have that, we actually, you know,basically need to go with the first principle thinking, which is like we always challenge, like status quo. And we also always ask a question that why do things, have to be done like this, and could we do it better. And , we also apply that into like our computer infrastructure, which is very important for us.

-Look, we've been part of some that, first principle thinking and, and how you ,you konw, really are focused on speed.Um lookm we're super thrilled of the work that we've done together, MI300X at xAI . I asked you guys to t. You know, can you talk about how youging the MI300 infrastructure? like, how has it worked? how did it come up for you?

-So if I use one world, right, that world is basically effortless. So as i mentioned.

-Can you say that word again?

-Indeed,effortless.to use the AMD GPU in your product. so,as I mentioned, we are a very samll team moving very fast. So for us, right, the most valuable resource is engineering time,right. so the opportunity cost is a mess, right. so with your team's help, we, we basically can, you know, do not need to like spend too much timea few of us engineers and your team, we successfully pushed one bery important product, Grok family model into product. And I remember, you know, when we first start to, collaborate together, right, I look at, there's a meeting on Friday. I say " Is this bery important? Can you just…can we just meet now?' Right. So after that, your engineers, adapted to our pace. So i always get like a phone call at like 9:00 PM or midnight, you konw, and then my partner was like asking me like, "who is calling?" I was like "Oh? Calling to ask about some question kernel." And he's like a violinist, so he's like," Oh, that almost never happen in orchestra. And then, and what is a kernel?" So because of that, we collaborate very closely, and then we can actually, you know, in few months we can push something, you know, into product. that's really impressive。

-You know, our engineers are always, reporting to me, you know, where are we on the Grok model performace. And you guys have moved super, super fast. so.Xiao, you know, the other thing is ,we're talking a lot about open ecosystems, and i know that you gus are a strong believe in open ecosystem. Can you talk a little bit about how ROCm and all of those community efforts have actually helped you?

-Sure. So as you know, right, our infer, inference structur is based on SGLAM, which is like a open source, very popular open source platform. And, also the major contributors and also, are also in, xAI. So while they are advancing. you know, most,optimized, inference system, they are also contribute a lot to the open source community. We upstream, you know, our innovations, to SGLang pubilc ripple. And at the same time, we also benefit a lot from the open sourcecommunity. They make.. they find bugs, they fix the codes, and then we merge into our, production. And that really helps a lot. I think it's very essential and we'll continue to commit to, contribute and work together with the open source community.

- Yeah. That's great. I think, the SGLang progress has been just a great example of how fast.So look, i know, you guys are always moving ahead. And I have a lot of products to talk about, with this audience today. Can you share a bit about your perspective of our collaboration and, like, what are you excited about? You know, what do you think about MI350 series and just all the work we're doing together.

-Sure, I'm actually very impressed by your, you know, yearly cadence about the new hardware. thinking about the future, I think we will continue to go back to first principles thinking. Imean, you are a pioneer, also, in semiconductors. So you konw that, what essentially we're doing is basically like a fancy waterworks here, except that it's not a water molecules. We are basically manipulating, like, electrons. Like, we're pumping electron in very high energy level, and then we guide it through the,you know, the channel of transistor to the gate of transistor, and then dissipate it to, you know, the ground. This is how we do compute. But you know, I think that this is not the end of it. This is the start of it. There are a way to do it like, probably 1000 or if not one million times more efficiently. And also on our side, one way of thinking about the computer is basically data. The data is like all the text a human has ever written. and I think now and in future will be like all the realities, all the truth in the world. And probably even further future, there will be like all the state of affairs that has not yet happeed, but it could happen, So we compress then all and then put them into like, you know, your USB disk or something like, you know. And then when you need to used it, you retrieve it and decompress it. This is how we think about it. But both sides have many innovation to do, but, we cannot, like, do it separately. So this is basically, from my point of view, from silicon to product, this is like the largestm you know, codesign of human history. And then, you know, at xAI, we are very happy to collaborate with vendors and AMD, right, you know, do this large codesign together, accelerate the iteration. And i hope that, you know, all the talents from the world should join on both sides.

-That's fantastic.Xiao, thank you so much for joining us today.Thank you for your partnership, with us on MI300. And we look forward to doing a lot more together.

Well, look, we have a full lineup today of new announcements across hardware, software, and solutions. So let's go ahead jump.

Now since launching MI300 less than two years ago, we're on an annual cadence of new Instinct accelerators. With the MI350 seies, we're delivering the largest generational performance leap in the history of Instinct. And we're already deep in development of MI400 for 2026 that is really designed from the grounds up as a rack solution. So today, I'm super excited to launch the MI350 series, our most advanced AI platformadship performance across the most demanding modles. This series, you'll hear us talk about the MI355 and MI350. They're actually the same silicon, but MI355 supports higher thermals and power envelopes so that we can even deliver more realworld performace. And thank you. My favorite part.

Here is MI355. This is our flagship product, and I'm showing this to you.It's pInstinct architecture. It supports new data formats like FP4. It used the latest HBM3E memory, and it has 185 billion transistors across 10 chiplets all integrated with our leadership 3D packaging. So what do you gus think?

Look, the MI350 series delivers just a massive 4x generational leap in AI compute to accelerate both training and inference. With an industryleading 288 gigabytes of memory, we can now run models up to 520 billion parameters on a signle GPU. The MI350 series also uses the same industrystandard UBB8 platform as MI300 and MI325.This is actually really import because it actually makes it super easy to deploy MI350 series into existing dat center infrastructure. Now if you look at the specs compared to the competition, 355 supports 1.6x more memory and deliver higher FLOPS across a wide range of AI data types. And especially if you look at FP6 and FP64, we're double the throughput. Now, waht does that mean? That means that you have leadership performance at both ends of the spectrum, wheout leading edge AI models or large-scale scientific simulationor engineering applications.Now at the platform level, an MI355X server has massive memory capacity and compute relative to the competition. We're taliking about 161 petaflops of FP4 compute. And 2.3 terabytes of HBM3E memory. And we have it in both air cooled and liquid cooled configs,giving customers the f;exobility to meet their specific thermal, power, and density needs.