Trust has always been at the heart of progress. It's how ideas take flight, from test to triumph. But trust also asks us to believe in one another, and that's why trust has to be earned. It's earned by relentlessly working together to solve the most important challenges and drive results. That's why, as we advance into the AI future, trust has to lead the way. So yes, we've built a roadmap we deliver on, on time. Yes, we build for the open ecosystem. And yes, we've built the broadest AI portfolio with CPUs, GPUs and FPGAs. But of all the things we build, trust is the most important.
Please welcome to the stage, Dr. Lisa Su, chair and CEO.
Good morning. How's everyone doing? Woo! Woo! It is great to be back here in Silicon Valley with so many friends, press, analysts, partners, and especially all of the developers who are here today. And a big welcome to everyone who's joining online from around the world for our Advancing AI 2025. Now, it's been an incredibly busy nine months since our last Advancing AI event. We launched lots of new AI data center PC and gaming products, but today, we have so much exciting news to share with you. I'd like to go ahead and get started. Now, you guys 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 in the world. I'm always incredibly 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 Salesforce or SAP, and many more, you're running on AMD infrastructure. 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 of new innovation that's going on with the new AI startups. For example, life sciences company 310.AI uses MI300X to train a model that turns simple text prompts into novel proteins to really help accelerate drug discovery. Our versatile AI adaptive SOCs are being used to build more efficient 5G networks and improve automotive driver safety. 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 my 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 scale deployments, 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 inference has grown significantly, driven by more capable models and new use cases that are increasing AI usage. We're also seeing an explosion of models. So of course you have the new frontier models from folks like OpenAI and Google, but you also have open models from Meta and DeepSeek and many others. And we're also seeing now a surge in new specialized models that are built from everything 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 purpose-built models, each tuned for specific tasks, industry, or use cases. And as AI does 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. 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 autonomously. They 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 global compute infrastructure. All of these agents are here to help us, and that requires lots of GPUs and lots of CPUs working together in an open ecosystem. Okay, so let's talk a little bit about the market. When we were here last year, we said that we expected the data center AI accelerator TAM to grow more than 60% annually to $500 billion in 2028. And frankly, for many of the analysts and folks, at the time, that seemed like a really big number. People were like, 'Do you really think it can be that big, Lisa?' And I said, 'Well, that's what we're seeing.' And what I can tell you, based on everything 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 going to be the driver of AI going forward and we can now see that inference inflection point. With all the new use cases and reasoning models, we now expect that inference is going to grow more than 80% a year for the next few years, really becoming the largest driver of AI compute. And we expect that high performance GPUs are going to be the vast majority of that market because they provide the flexibility and programmability that you need as models are continuing to evolve and 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 every 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 focused on. Our strategy is really 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 model and the right use case. Second, we're investing heavily in an open developer first ecosystem, and you're going to hear us talk about open a lot today. We're really supporting every major framework, every library, every model to bring the industry together in open standards so that everyone can contribute to AI innovation. And third, we're delivering full stack solutions. We're building, we're forging partnerships. You're going to hear from some of our partners about our ecosystem today to really put all of these elements together. So now let me just give you a little bit of color. From a portfolio standpoint, we offer the most complete suite of computing elements end to end for this vision. That includes CPUs, GPUs, DPUs, NICs, FPGAs, and adaptive SOCs. No matter where AI runs or how much compute you need, AMD has the right solution. Next, let's talk about open. There are a lot of developers in this audience and online, so this is really talking to you. Thank you 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 committed to openness across hardware, software, and solutions. And when you just take a look back, some of the most important breakthroughs in tech actually started out closed if you think about things like 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 takes off when things are open. Linux surpassed Unix as a data center operating system of choice when global collaboration was unlocked. Android's open platform helped scale mobile computing to billions of users. And in each case, openness delivered 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 the scale, adoption, and impact of AI over the coming years. Now, we also recognize that these AI systems are getting super complicated and full stack solutions are really critical. So to deliver full stack AI solutions, we've significantly expanded 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 strengthened our software stack, acquiring leaders 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 momentum in the data center. Since launching in 2017, EPYC has transformed the data center. Today, EPYC is trusted by the world's largest cloud providers and businesses to run their most important workloads. EPYC powers everything 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 exited 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's pretty cool. 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've added a lot of new 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 going to 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 what 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 a developer first mentality with ROCm. When you hear me talk to 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 going to hear a lot about that from Vamsi today. And then for those of you who are going to be able to stay with us this afternoon, we have 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.
Hello, Xiao.
I'm good. How are you? 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 xAI. You know, you guys are really at the forefront of developing state-of-the-art AI models. You're going super fast. Can you share a little bit about what your team does and how you are managing all this?
Sure, sure. Yeah, at xAI, we have a very small team, and we're moving very fast, and we're following first principles. Basically, we are advancing the Grok family of models for maximum truth-seeking. And to have that, we actually need to go with first principle thinking, which is we always challenge the status quo. And we also always ask the question: why do things have to be done like this, and could we do it better? And we also apply that to our compute infrastructure, which is very important for us.
Absolutely. Look, we've been part of some of that first principle thinking and how you really are focused on speed. We're super thrilled of the work that we've done together on MI300X at xAI. I asked you guys to give us a shot. Can you talk about how you're leveraging the MI300 infrastructure? How has it worked? How did it come up for you?
Yeah, yeah, yeah. If I use one word, that word is basically effortless.
Can you say that word again?
Yeah, indeed, it's effortless to use the AMD GPU in your product. As I mentioned, we are a very small team moving very fast. So for us, the most valuable resource is engineering time, right? The opportunity cost is immense. With your team's help, we basically did not need to spend too much time. Just a few of us engineers and your team, we successfully pushed one very important product, the Grok family model, into production. And I remember when we first started to collaborate, I looked at a meeting on Friday and said, 'Is this very important? Can we just meet now?' After that, your engineers adapted to our pace. So I always get a phone call at 9:00 PM or midnight, and my partner was asking me, 'Who is calling?' I was like, 'Oh, it's AMD calling to ask about some kernel question.' And he's a violinist, so he was like, 'Oh, that almost never happens in an orchestra. And what is a kernel?' Because of that, we collaborate very closely, and in a few months we could push something into production.
That's really impressive. I've been impressed because our engineers are always reporting to me where we are on the Grok model performance, and you guys have moved super, super fast. Xiao, the other thing is, we're talking a lot about open ecosystems, and I know that you guys are strong believers in open ecosystems. Can you talk a little bit about how ROCm and all of those community efforts have actually helped you?
Sure, sure. So as you know, our inference infrastructure is based on SGLang, which is an open source, very popular open source platform. The major contributors are also at xAI. While they are advancing the most optimized inference system, they also contribute a lot to the open source community. We upstream our innovations to the SGLang public repository. And at the same time, we also benefit a lot from the open source community. They find bugs, they fix the codes, and then we merge that into our production infrastructure. 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.
No, that's great. I think the SGLang progress has been just a great example of how fast things go. 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 what are you excited about? What do you think about the MI350 series and all the work we're doing together?
Sure. I'm actually very impressed by your yearly cadence of new hardware. Thinking about the future, I think we will continue to go back to first principles thinking. You are a pioneer in semiconductors, so you know that what we're essentially doing is like fancy waterworks, except it's not water molecules. We are manipulating electrons. We pump electrons to a very high energy level, and then we guide them through the channel of a transistor to the gate, and then dissipate them to the ground. That's how we do compute. But I think this is not the end of it. This is the start of it. There are ways to do it probably 1,000 or even one million times more efficiently. And on our side, one way of thinking about the computer is basically compression of data. The data is all the text a human has ever written. And I think now and in the future, it will be all the realities, all the truth in the world. And probably even further in the future, it will be all the states of affairs that have not yet happened, but could happen. So we compress them all and put them into a USB disk or something. Then when you need to use it, you retrieve it and decompress it. That's how we think about it. But both sides have many innovations to do, and we cannot do it separately. So from my point of view, from silicon to product, this is the largest co-design in human history. At xAI, we are very happy to collaborate with vendors like AMD to do this large co-design together and accelerate the iteration. I hope that 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.
Thank you, Xiao. Well, look, we have a full lineup today of new announcements across hardware, software, and solutions. So let's go ahead and jump right in. Now, since launching MI300 less than two years ago, we're on an annual cadence of new Instinct accelerators. With the MI350 series, 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 ground up as a rack level solution. So today, I'm super excited to launch the MI350 series, our most advanced AI platform ever that delivers leadership performance across the most demanding models. This series, you'll hear us talk about the MI355 and the MI350. They're actually the same silicon, but MI355 supports higher thermals and power envelopes so that we can deliver even more real-world performance. And thank you, Drew. My favorite part. Here is MI355. This is our flagship product, and I'm showing this to you. It's powered by our latest 4th Gen Instinct architecture. It supports new data formats like FP4. It uses the latest HBM3E memory, and it has 185 billion transistors across 10 chiplets all integrated with our leadership 3D packaging. So what do you guys think? All right. Thank you, Drew. Look, the MI350 series delivers just a massive 4x generational leap in AI compute to accelerate both training and inference. With an industry-leading 288 gigabytes of memory, we can now run models up to 520 billion parameters on a single GPU. The MI350 series also uses the same industry-standard UBB8 platform as MI300 and MI325. This is actually really important because it makes it super easy to deploy MI350 series into existing data center infrastructure. Now, if you look at the specs compared to the competition, MI355 supports 1.6x more memory and delivers higher FLOPS across a wide range of AI data types. And especially if you look at FP6 and FP64, we have double the throughput. Now, what does that mean? That means that you have leadership performance at both ends of the spectrum, whether you're talking about leading edge AI models or large-scale scientific simulation or engineering applications. Now, at the platform level, an MI355X server has massive memory capacity and compute relative to the competition. We're talking 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 flexibility to meet their specific thermal, power, and density needs. Now let's look at some of the performance. We set an ambitious goal with MI350 series to deliver a 35x generational increase in AI performance, and today I'm proud to say that we've delivered that. On Llama 3.1, MI355 delivers 35x higher throughput when running at ultra low latencies, which is required for some real-time applications like code completion, simultaneous translation, and transcription. We also deliver significantly higher performance across a wide range of AI applications, things like chatbots, or content generation, or summarization, or conversational AI. We can see performance up to 4.2x higher gen-on-gen. And now when you look across a wide range of models, we see great performance as well. In DeepSeek and Llama 4 Maverick, we're seeing things like triple the tokens per second gen-on-gen. So that level of performance drives faster responses and the ability to serve more users with much, much greater efficiency. Now let's take a look at the competitive performance. When running DeepSeek R1 or Llama 3.1, MI355 delivers leadership throughput using open source frameworks like SGLang and vLLM. We're generating up to 30% more tokens per second compared to B200, and actually matching the performance of the significantly more expensive and complex GB200, even when the competition is using their latest proprietary software stack. This is actually pretty cool because it tells you a couple of things. It first says that we have really strong hardware, which we always knew, but it also shows that the open software frameworks have made tremendous progress, to the point where they are outperforming a closed vendor specific ecosystem. And when you combine all of that performance with lower CapEx, what we're seeing is MI355 can deliver up to 40% more tokens per dollar than competing solutions. 40% more tokens per dollar. That means higher throughput, greater efficiency, much better TCO for cloud and enterprise, and it really makes MI355 the best choice in the industry for inference at scale. Now, one of our earliest partners to deploy Instinct broadly was Meta. To share more on our work together, please welcome Meta VP of Engineering, Yee Jiun Song, to the stage.
YJ. Thank you for having me, Lisa.
Hey, thank you so much for being here. We're so excited about the work that we're doing together. You know, look, we have so much respect. Meta has been an incredible leader in AI across infrastructure, models, and services. I get to talk to YJ a lot. He gives us good feedback. You're delivering all of this capability at amazing scale, and so it's been our privilege to be your partner across EPYC and now Instinct. Can you talk a little bit about our collaboration?
Well, first, thank you for having me here, Lisa. I'm thrilled to be here and excited to see all the progress that you and the AMD team are making. We're seeing incredible advancements that started with your EPYC products and now extending to all of your AI offerings. I think AMD and Meta has always been strongly aligned on vision and roadmap execution. This means that we have very close co-engineering, performance tuning. We troubleshoot problems together, and we're able to deploy optimized systems at scale. So our teams really see AMD as a strategic and responsive partner, and someone that we really rely on.
Well, we love working with your engineering team, and you know that. We love the feedback, and you also hold a high standard. You were one of our earliest partners in AI. As your demands continue to scale, you've talked about your usage of MI300. What are you doing today and what are your plans with MI350 going forward?
Yeah. So AI has been core to many of the user experiences across all of Meta's products for a long time, across Facebook, Instagram, WhatsApp. And now, of course, with Llama and Meta AI, AI has become even more important than before. It's been fantastic to see our collaboration on MI300X come to fruition. MI300X accelerators today are a key part of our infrastructure. We've deployed this quite broadly for Llama 3 and Llama 4 inference due to its high performance and excellent performance per TCO. As we gain experience with MI300X, we're also expanding the workloads that we run on them. So we're now using MI300X both for training and inference of different ranking and recommendation workloads which are critical to our business. We're also quite excited about the capabilities of MI350X. We like that it brings significantly more compute power, next generation memory, and support for FP4, FP6, all while maintaining the same form factor as MI300 so we can deploy quickly.
Thank you, YJ. Thank you, by the way, for your trust. I know that to deploy us on more workloads requires effort on both sides, so we really appreciate that. Meta has really been a leader developing frontier models. If you think about AI across all of your applications, tell us a little bit about what you're seeing. You're at the front there. What are you seeing in applications? What are you seeing in your compute investments?
Yeah. So I think the AI work at Meta that gets the most attention is probably the Llama models, though we are committed to developing frontier models with our Llama efforts. But that's really just the tip of the iceberg. We're seeing growth of AI workloads across all of our different products. AI is not only improving our existing products, but also allowing us to develop entirely new products. All of this is driving investments in compute infrastructure at a scale that's quite unprecedented. We're building data centers and filling them up faster than I've ever seen. Our entire infrastructure team is sprinting to ensure we have the capacity to build the next great AI models and then take advantage of those models to deliver value to our users. The result of this incredible capacity buildup is that we really care about performance and making sure that we get the best bang for the buck for our investments.
Well, we've been part of a few of those sprints.
So, YJ, we've also worked very closely on the software side. You know, PyTorch, ROCm, the open hardware environment. How do you think about open in your strategy?
Yeah. So I think our collaboration has spanned both software and hardware for many years at this point. I think most recently, since 2021, we've partnered closely to enable ROCm through PyTorch to ensure that developers can leverage AMD GPUs with PyTorch's ease of use right out of the box.
Beginning last year, we've worked closely on improving ROCm's communication library, RCAL, which is critical for AI training.
We really appreciate that partnership, by the way.
Us too. Beyond PyTorch, Meta contributes heavily to the open source community. An example here is work on compiler frameworks, such as Triton, which allows us to write code once and then run on different accelerator families. Now, of course, we also collaborate on optimizing Llama models to run well on AMD GPUs. Operating at scale also requires that the accelerators we buy be compatible with our network and data center infrastructure. Here, we rely on our common infrastructure hardware racks to be the integration point between our accelerators, the network, and the data centers themselves. This is one of the reasons why we've been able to introduce MI300 into our production environment so quickly.
Yes. No, absolutely. I think the OCP work is fantastic. Look, we're super excited about our partnership, everything that we're doing together, and it feels like we're just starting the ramp of MI350.
But in our business, we're always talking about the future.
Absolutely. You guys are always asking us what's next.
Absolutely. I'm actually going to preview MI400X a little bit later in the show. So can you just talk about where you see AI going in the future and how that shapes what you need from partners like us?
So AI is driving massive growth in infrastructure demand, but actually, it's not just about the size of the demand or the amount of capacity. The type of workload is also changing very rapidly. As an example, not so long ago, as an industry, we were very focused on pre-training.
But towards the end of last year, we started to see the emergence of test time inference and reinforcement learning and other new workloads that demand huge amounts of computation. We also started to see the rise of mixture of expert models that placed high demands on network interconnection speeds and the performance of the collective communication primitives we just talked about. And beyond generative AI, we are also finding that our recommendation systems are getting more complex. The direct implication of these rapid changes is that Meta and AMD will have to work even more closely together to define our accelerator and network roadmaps for the future. I can't wait to hear what you're about to share.
Well, you know everything I'm about to share. But I will say that I do remember sitting in your office and asking you, 'So YJ, tell me what's going to happen when the workloads...' and you were like, 'Well, you know, be flexible.' That's absolutely true. Thank you, YJ. Thank you. We really, really appreciate the partnership.
It's wonderful working with you and the entire team, and thank you for all that we're doing together.
Thank you.
All right. So now let's turn to training. In addition to all the work we've done to improve inference, we've also made a lot of optimizations for training, and I'm happy to say we've seen some fantastic results. MI355 delivers significantly better training performance than MI300. And when you look at pre-training, for example, where foundational models are built from the ground up, MI355 is delivering up to 3.5X higher throughput across a range of models and data formats. And in fine tuning, we're delivering up to 2.9X more performance gen-on-gen, which enables faster iteration cycles and reduces the time from model development to model deployment. Now, comparing to the competition, MI355 pre-training performance is actually on par with B200 across a range of model sizes and data formats. And I actually think that's very good considering how new MI355 is. Now, in fine tuning, we just saw some of the latest MLPerf benchmarks that were out there, and MLPerf is largely considered the gold standard for training benchmarks. We see that MI355X actually outperforms both B200 and GB200 when we're talking about completing the benchmark up to 13% faster compared to the latest published results. So that just tells you how much progress we've made in training. And now, as we talk about solutions, we said we want to make this super easy to use. So with the MI350 series, OEMs and ODMs are launching racks built entirely on AMD technology for the first time. We're combining fifth gen EPYC CPUs, Instinct MI350 GPUs, and our Pensando NICs in an integrated solution. And these are all OCP compliant designs, so they drop right into existing infrastructure. And in some of the densest environments, we have liquid cooled racks that can scale up to 96 or 128 GPUs that deliver up to 2.6 exaflops of FP4 compute and 36 terabytes of HBM3e memory. And on the enterprise side, we can do air-cooled systems that support up to 64 GPUs and integrate all of that into an existing infrastructure. So this is the kind of flexibility and range that our customers really want. What they want is to be able to take the technology, get it into production, get it into the data centers as quick as possible with as little work, as little disruption. And that's exactly what we can do with MI350. Now, one of our most strategic cloud partners that's building with AMD across the stack is Oracle. To share more about our work together, please welcome Mahesh Thiagarajan, executive vice president at OCI.
Hello, Mahesh.
Hey, it's wonderful. Thank you for being here. We so appreciate the partnership with OCI. You guys have been with us across the board.
You guys are frankly at the center of AI compute, tremendous momentum.
Powering some of the largest training and inference clusters. So, look, as you look through what's facing you in all of these deployments, what's most important to you?
Look, I'm truly honored, first, to be actually working so closely with AMD and building this AI infrastructure at a relentless pace together. Fundamentally, to solve the next frontier of challenges at the intersection of cloud and AI, we need to do a deep integration across the entire stack, starting from power, to compute, to network, to storage, to truly use every last ounce of the performance that's available. So, let me break that down a little bit. When we talk to customers about compute, what we see is that the most demanding training and inferencing workloads need the exceptional reading of the CPU and GPU memory. This is where I think the AMD Infinity Fabric actually truly enables offering the performance between moving the data sets really close to the accelerators at AI speeds, and we're seeing customers seeing tremendous value. The second thing, which I think is super important, I'm very passionate about what AMD is doing here, is really around the high performance networking that comes close to delivering these large training clusters. And fundamentally, when you think about an AI supercluster, it is operating as one giant supercomputer, really looking for that ultra-low latency, extreme high performance bandwidth, and truly operating as one supercomputer. And so, that performance across these nodes really matters to complete that task. And what we partner with and we work with AMD a lot on is on the networking technologies. And for example, the Pensando work that we've been doing for a while truly enables the security of these AI workloads and actually is powering the performance that we're seeing.
Yep. Yeah. No, that's fantastic. I think... Thank you. We love that vision overall of putting all these pieces together. By the way, that's exactly our philosophy as well.
That you need CPUs, GPUs, networking coming together. Now, OCI was one of our early adopters of MI300X. It's been great to see some of the customer response. Can you talk a little bit about how AMD Instinct looks in Oracle Cloud?
Look, MI300X on Oracle Cloud Infrastructure is a very deep integration. We've seen massive demand from both AI native companies and large frontier model companies actually doing work on top of OCI today. Now, the support model is actually where we work partner well together to offer that fantastic experience, where when a customer comes looking for a cluster, in a matter of minutes, they're able to get their AMD Instinct machines, truly with what AMD brings to the table. The latest and the greatest ROCm innovation, the updates, everything's available out of the box, step one. Two, what we try to do is also ensure that customers are getting the latest performance benefits of everything that you guys are doing on ROCm very regularly on a monthly update. So, the customers actually get the best of AMD immediately. And third, is the latest edition of your PyTorch and vLLM support. We've actually seen acceleration of some of our customers...
...who've been waiting for that support. So, they're like, 'Oh man, this is exciting, so let's go use that platform.' And so, you know, look, some of the largest names, largest customers, who you've all heard about are all running AMD Instinct on OCI, and they're having a great experience.
It's wonderful, Mahesh. Look, I really have to thank you and your team. I know that this has very much been about getting exactly what customers need.
And you guys have been super, super agile in that. Now, when you think about Oracle and your adoption of technology, we talked about MI300. You're actually now leading with us on MI355. I know there's a lot of interest there. Can you talk a bit about just the evolution of our partnership and what you're seeing?
Look, I think our partnership probably started about a decade ago, right? I think we've been partnering for a long time...
...but I think it started heating up around a decade ago. And it started with us actually using AMD EPYC for our databases.
Right? Our Oracle likes the database machines now. Back since then, we've been using AMD EPYC. We've seen tremendous performance where we're seeing 3X higher transaction throughputs and 3.6X faster analytic queries. And the great news about that is that's actually available not only on OCI, but on premises, other cloud partners that are actually supporting Oracle's databases, including our latest Oracle Autonomous Database. So, it's everywhere. And then, something that's very personal to me is our Pensando partnership that truly enables a hardware-based network virtualization in our entire cloud, which is a fundamental unique innovation that offers security and high performance for every customer that runs on OCI today, and that's with AMD. And obviously, 18 months ago, we did the AMD Instinct partnership. Lots of customers are growing. And we think there's tremendous demand, projecting about a 10X growth over the next year, really trying to drive the AMD Instinct platform on OCI. And the most exciting part for me is that we're announcing our partnership on MI355, truly bringing the latest out to the cloud with support for zettascale clusters.
By the way, did you guys hear that? Support for zettascale clusters?
So, I'm truly excited about that, because I think when I talked about that deep integrated compute network storage, we're going to support AMD MI355 and more importantly, we're actually going to go live in a couple of months with over 27,000 GPUs in a single cluster available on OCI in two months.
That's fantastic. Thank you. Now, I'm asking everybody here, this is also about the future and, you know, Oracle has actually been leading on this concept of building gigascale data centers. And we're talking about what we have to do from a systems standpoint. Tell us what it means to build gigascale data centers and how we can participate in that.
Yeah. No, look, I'm an infrastructure nerd so I'll talk about power. Look, gigascale, I think if you talked about this phrase two years ago everybody would have been like, 'What are you talking about?' Right? It's insane. But I think the biggest challenge that we see is still power. Power is the fundamental bottleneck that still exists and the speed at which we can build them. But I think Oracle's partners come here where we've been a big partner with all of the utilities and other businesses and industry verticals so that actually has been very helpful. And second, we are making investments in sustainable energy, be it green energy around geothermal, wind, solar, and we're also looking at new small nuclear reactors that can power this. The second thing is about time to market. One of the things that OCI pioneers is actually bringing our cloud infrastructure in like three racks. So we spend a ton of time tuning time to market. And second, bringing that price performance value to our customers. And for me, today we're operating at over 100 regions so we're able to reach a far-reaching audience. But I think going back to the price performance message, I really think that's why AMD and Oracle work together.
Yeah. We're bringing value to customers. That's what we do. It's all about the price performance and today, with that price performance value, customers actually enjoy the AMD plus Oracle partnership.
And lastly, we're really excited and looking forward to your MI450X platform. I'm seeing some of the specs and I think it's going to be truly special.
Fantastic. Mahesh, thank you so much. Thank you for the partnership overall.
Any time. And I look forward to everything we're going to do together.
Absolutely. Thank you so much, Lisa.
Appreciate it. Thank you.
All right. So, look, customer excitement for MI350 is very strong based on the performance and cost per token advantages. I'm happy to announce that MI355 production shipments actually started earlier this month and we have the initial wave of partners on track to launch platforms and public cloud instances here in the third quarter. So really, really excited about MI350. Now, another important focus for us is sovereign computing. Around the world, we're partnering with national governments and research institutes to help build the high performance computing and AI infrastructure that is really critical for their economies. And the goal really goes far beyond just building domestic compute capacity. It's really about using AI to power public services, research, and national programs that create societal impact. To get there, governments are actually prioritizing resilient infrastructure. They want open standards, they want flexible architectures, and they want a diverse ecosystem of technology partners. Today, we have more than 40 active engagements globally powering critical public agencies, national computing centers, and sovereign AI activities. From the world's fastest super computers in the US to the rapid expansion of high performance computing across Europe, Asia, and the Middle East, to a wave of sovereign AI deployments around the world. This is a growing part of the market and we are increasingly spending more time helping nations build their computing strategy and infrastructure. One of the best examples of our progress is in Europe with our Silo AI team. Silo is our AMD AI lab, but they're also a solutions factory collaborating closely with governments, industries, and research institutions to develop models and applications aligned with national priorities and optimized on our hardware. Silo is working across Europe collaborating with companies like Allianz, Nokia, Philips, and Unilever, advancing open multilingual LLMs with the European Commission and pushing frontier model research on the AMD powered LUMI supercomputer. They're also playing a very important leadership role in the open source AI community contributing to models and partnering with leading AI innovators like Aleph Alpha, Mistral, and NXAI. Now another extremely exciting example of our sovereign efforts is our work with Humain, a new company with an ambitious vision to build advanced, locally developed AI in the Middle East. To share more about our work, please welcome Tareq Amin, CEO of Humain.
Good morning, Lisa.
Hello, Tareq. Good morning.
Good morning, everyone. It is great to have you here. Thank you so much for joining us. We're so excited about our partnership together.
Well, first of all, thank you very much for inviting me here. I don't need to tell you this but AMD is really an important partner for Humain, important partner for Saudi Arabia, and also important partner for the entire larger ecosystem of AI companies.
Well, look, you guys are on an exciting mission. I had the pleasure and honor to be with you in the kingdom just last month and the vision that you're laying out, launching Humain, it's such an important moment for the kingdom and just taking sovereign AI to the next level. So, can you share with us. Just tell me about your vision, your plans, everything.
So Lisa gave me four minutes. By the way, this is the biggest challenge...
This is the biggest challenge I have. But I wish I could share with you what we have done last month. I'll take a perspective just to tell you the entire story and the partnership that we're doing with AMD to redefine the entire AI infrastructure ecosystem. In the US, I had the opportunity to build digital infrastructure across 22 cities. I moved to India where I learned how to scale things. I moved to Tokyo where I built technology that was in a research paper, realized it, and the story gets completed with Humain. Humain and Saudi Arabia came together through the consolidation of various enterprises in the country, and also one government entity that was developing large language models. Our obsession is about disruption via technology. The way we pick partners is not based on what I call transactional selections. When we met Lisa and her team, we really hit it off because we both agreed that we're going to co-own the outcomes. It was very, very important that co-owning the outcomes and having a skin in the game, taking a risk to build something that is good for humanity was a very, very important mission. So today, in front of all of you, though we've talked about this, the announcement about the joint venture with AMD, I am really thankful for your support for what we need to do. But I want to give you a glimpse of what this really means. We are committed, and when we looked at the advantage of what Saudi Arabia could really do, by 2030, the deficit in power is estimated to be around 100 gigawatts. No matter what you do, you will still need power to build the capacity that we need for AI. This is an added advantage that we thought we could really help and we could participate in this AI global ecosystem. We have an abundance of land, an abundance of power, a mixture of renewable as well as traditional energy, and a really very young society that is hungry to learn. So we thought this could be great. Our commitment for all AI developers and AI companies: what if we reduce your cost of ownership by 30%? From whatever you could achieve as the lowest worldwide cost, I'm committing to make that together with Lisa the lowest cost.
That sounds like a good commitment.
So, we're really, really happy. This is a game changing moment. We're really privileged that this joint venture is going to be a game changer. 2030, 1.9 gigawatts, 2034, six gigawatts. It starts in Riyadh, but it doesn't stop there. We will go and look at other global opportunities to build our infrastructure.
You know, Tareq, I want to just point out some of the things that you said. We've talked about the need for power, we've talked about the need for speed, and we've talked about the need for efficiency in what we're doing. I think what impresses me the most about the work that we're doing together is you really have like a clean sheet of paper to talk about what's next. So we've talked about a lot of AI infrastructure both in the kingdom and outside of the kingdom. Can you just talk about how some of the milestones that we have in place?
So, I think as soon as we really crafted this agreement, the timing of launching Humain was not coincidental. We were really happy that it coincided with a presidential visit into Saudi Arabia to talk about relationship and partnership they're doing with technology companies such as AMD. We have already started the construction of two large campuses, 11 data centers, each one of them of 200 megawatt capacity each. I will tell you, Lisa, almost on a weekly basis, 'Tareq, we need to move faster, we need to move faster.' So I really appreciate your spirit.
