Hello and welcome to this unique moment in human history. A moment where what's possible might soon forget what's impossible, where any game you play now has the power to play by your rules. Where AI not only helps model the possibilities of what a city can be, but make sure our kids never forget what our cities used to be. A moment where no hope meets the treatment plans of AI genomes. Where no driver officially has better reflexes than any driver who's ever lived. And where no signal can no longer stop you from sharing.
Show me. Show me. Show me this.
A moment where AI is helping design a renewable energy source as powerful as the sun itself and helping make travel time across the Atlantic just another puddle jumper. But as fast as everything's changing, there's one thing that won't. We're working tirelessly to create a world where the most advanced AI capabilities end up in the right hands. Yours. So just keep walking even.
So ladies and gentlemen, it is my privilege to welcome to the stage a globally respected technologist, an industry defining CEO and a leader whose work continues to shape the very trajectory of modern computing. Ladies and gentlemen, please join me in welcoming to the stage chair and CEO of AMD, Dr. Lisa Sue.
Thank you. Thank you.
All right. What an audience. How are you guys doing tonight? That sounds wonderful. First of all, thank you, Gary, and welcome to everyone here in Las Vegas and joining us online. It's great to be here with all of you to kick off CES 2026. And I have to say, every year, I love coming to CES to see all the latest and greatest tech and catch up with so many friends and partners. But this year, I'm especially honored to be here with all of you to open CES. Now, we have a completely packed show for you tonight, and it will come as no surprise that tonight is all about AI. Although the rate and pace of AI innovation has been incredible over the last few years, my theme for tonight is you ain't seen nothing yet. We are just starting to realize the power of AI. And tonight I'm going to show you a number of examples of where we're headed. And I'll be joined by some of the leading experts in the world from industry giants to breakthrough startups. And together we are working to bring AI everywhere and for everyone. So let's get started.
At AMD, our mission is to push the boundaries of high performance and AI computing to help solve the world's most important challenges. Today, I'm incredibly proud to say that AMD technology touches the lives of billions of people every day. From the largest cloud data centers to the world's fastest supercomputers to 5G networks, transportation, and gaming, every one of these areas is being transformed by AI. AI is the most important technology of the last 50 years, and I can say it's absolutely our number one priority at AMD. It's already touching every major industry. Whether you're going to talk about healthcare or science or manufacturing or commerce and we're just scratching the surface. AI is going to be everywhere over the next few years. And most importantly, AI is for everyone. It makes us smarter. It makes us more capable. It enables each one of us to be a more productive version of ourselves. And at AMD, we're building the compute foundation to make that future real for every company and for every person.
Now, since the launch of Chat PPT a few years ago, I'm sure we all remember the first time we tried it. We've gone from a million people using AI to now more than a billion active users. This is just an incredible ramp. It took the internet decades to reach that same milestone. Now what we are projecting is even more amazing. We see the adoption of AI growing to over five billion active users as AI truly becomes indispensable to every part of our lives. Just like the cell phone and the internet of today. Now the foundation of AI is compute. With all of that user growth, we have seen a huge surge in demand in the global compute infrastructure, growing from about one zettaflop in 2022 to more than 100 zettaflops in 2025. Now, that sounds big. That's actually a hundred times in just a few years. But what you're going to hear tonight from everyone is we don't have nearly enough compute for everything that we can possibly do. We have incredible innovation happening. Models are becoming much more capable. They're thinking and reasoning. They're making better decisions. And that goes even further when we extend that to agents overall. So to enable AI everywhere, we need to increase the world's compute capacity another hundred times over the next few years to more than 10 yottaflops over the next five years. Now let me take a survey. How many of you know what a yottaflop is? Raise your hand, please. A yottaflop is a one followed by 24 zeros. So 10 yottaflops is 10,000 times more compute than we had in 2022. There's just never ever been anything like this in the history of computing. And that's really because there's never been a technology like AI.
Now to enable this you need AI in every compute platform. So what we're going to talk about tonight is the whole gamut. We're going to talk about the cloud where it runs continuously delivering intelligence globally. We're going to talk about PCs where it helps us work smarter and personalize every experience that we have. And we're going to talk about the edge where it powers machines that can make real-time decisions in the real world. AMD is the only company that has the full range of compute engines to make this vision a reality. You really need to have the right compute for each workload. And that means GPUs, that means CPUs, that means NPUs, that means custom accelerators. We have them all. And each of them can be tuned for the application to give you the best performance as well as the most cost-effective solution. So tonight we're going to go on a journey. You're going to go with me through several chapters as we showcase the latest AI innovations across cloud, PCs, healthcare, and much more. So let's go ahead and start with the first chapter, which is the cloud.
The cloud is really where the largest models are trained and where intelligence is delivered to billions of users in real time. For developers, the cloud gives them instant access to massive compute, the latest tools, and the ability to deploy and scale as use cases take off. The cloud is also where most of us experience AI today. So whether you're using Chat GPT or Gemini or Grok or you're coding with co-pilots, all of these powerful models are running in the cloud. Now today, AMD is powering AI at every level of the cloud. Every major cloud provider runs on AMD EPYC CPUs and eight of the top 10 AI companies use Instinct accelerators to power their most advanced models and the demand for more compute is just continuing to go up. Let me just show you a few graphs. Over the past decade, the compute needed to train the leading AI models has increased more than four times every year. And that trend is just continuing. That's how we're getting today's models that are dramatically smarter and more useful. At the same time, as more people are using AI, we've seen an explosion over the last two years of inference, growing the number of tokens a hundred times, really hitting an inflection point. You can just see how much that inference is really taking off. And to keep up with this compute demand, you really need the entire ecosystem to come together. So what we like to say is the real challenge is how do we put AI infrastructure at yacht scale and that requires more than just raw performance. It starts with leadership compute, CPUs, GPUs, networking coming together. It takes an open modular rack design that can evolve over product generations. It requires high-speed networking to connect thousands of accelerators into a single unified system and it has to be really easy to deploy. So we want full turnkey solutions. That's exactly why we built Helios, our next generation rack scale platform for the yacht scale AI era. Helios requires innovation at every single level, hardware, software and systems. It starts with our engineering teams who designed our next generation Instinct MI455 accelerators to deliver the largest generational performance increase we've ever achieved. MI455 GPUs are built using leading edge 2nm and 3nm process technologies and advanced 3D chiplet packaging with ultra fast high bandwidth HBM4 memory. This is integrated into a compute tray with our EPYC CPUs and Pensando networking chips to create a tightly integrated platform. Each tray is then connected with high-speed ultra accelerator link protocol tunneled over Ethernet which enables the 72 GPUs in the rack to function as a single compute unit. And then from there we can connect thousands of Helios racks to build powerful AI clusters using industry standard ultra Ethernet and Pensando programmable DPUs that can accelerate AI performance even more by offloading some of the tasks from the GPUs. Now we are at CES. It is a little bit about show and tell. So I am proud to show you Helios right here in Vegas. The world's best AI rack.
Is that beautiful or what? Now, for those of you who have not seen a rack before, let me tell you, Helios is a monster of a rack. This is no regular rack, okay? This is a double wide design based on the OCP open rack wide standard developed in collaboration with Meta and it weighs nearly 7,000 pounds. So Gary, it took us a bit to get it up here, just so you know. But we wanted to show you what is really powering all this AI. It is actually more than two compact cars. Now the way we've designed Helios was really working closely with our lead customers and we chose this design so that we could optimize serviceability, manufacturability and reliability for next generation AI data centers. Now let me show you a few other things. At the center of Helios is the compute tray. So let's take a closer look at what one of those trays looks like. Now, I can tell you I probably cannot lift this compute tray, so it had to come out. But let me just describe it a little bit. Each Helios compute tray includes four MI455 GPUs, and they're paired with the next gen EPYC Venice CPU and Pensando networking chips. And all of this is liquid cooled so that we can maximize performance. At the heart of Helios is our next generation Instinct GPUs. And you guys have seen me hold up a lot of chips in my career. But today I can tell you I am genuinely excited to hold up this chip. So let me show you MI455X for the very first time. MI455 is the most advanced chip we've ever built. It's pretty darn big. It has 320 billion transistors, 70% more than MI355. It includes 12 2nm and 3nm compute and IO chiplets and 432 GB of ultra fast HBM4 all connected with our next gen 3D chip stacking technology. So we put four of these into the compute trays up here. And then driving those GPUs is our next generation EPYC CPU code named Venice. Venice extends our leadership across every dimension that matters in the data center. More performance, better efficiency, and lower total cost of ownership. Now, let me show you Venice for the first time. I have to say this is another beautiful chip. I do love our chips. So, I can say that for sure. Venice is built with 2nm process technology and features up to 256 of our newest high performance Zen 6 cores. And the key here is we actually designed Venice to be the best AI CPU. We doubled the memory and GPU bandwidth from our prior generation. So Venice can feed MI455 with data at full speed, even at rack scale. So this is really about co-engineering. And we tie it all together with our 800 gig Ethernet Pensando Volcano and Selena networking chips delivering ultra high bandwidth as well as ultra low latency. So tens of thousands of Helios racks can scale across the data center. Now, just to give you a little bit of the scale of what this means, that means that each Helios rack has more than 18,000 CDNA 5 GPU compute units and more than 4600 Zen 6 CPU cores, delivering up to 2.9 exaflops of performance. Each rack also includes 31 terabytes of HBM4 memory, an industry-leading 260 terabytes per second of scale-up bandwidth, and 43 terabytes per second of aggregate scale-out bandwidth to move data in and out incredibly fast. Suffice it to say, those numbers are big. When we launch Helios later this year, and I'm happy to say Helios is exactly on track to launch later this year, we expect it will set the new benchmark for AI performance. And just to put this performance in context, just over six months ago, we launched MI355 and we delivered up to 3x more inference throughput versus the prior generation. And now with MI455, we're bending that curve further, delivering up to 10 times more performance across a wide range of models and workloads. That is game changing. MI455 allows developers to build larger models, more capable agents, and more powerful applications. And no one is pushing faster and further in each one of these areas than OpenAI. To talk about where AI is headed and the work that we're doing together, I'm extremely happy to welcome the president and co-founder of OpenAI, Greg Brockman, to the stage.
Greg, it is so great to have you here. Thank you for being here. You know, OpenAI truly started all of this with the release of Chat GPT a few years ago and the progress you've made is just incredible. We're absolutely thrilled about our deep partnership. Can you just give us a picture of where are things today? What are you seeing and how are we working together?
Well, first of all, it's great to be here. Thank you for having me. Chat GPT is very much the overnight success that was seven years in the making, right? We started OpenAI back in 2015 with a vision that deep learning could lead to artificial general intelligence, to very powerful systems that could benefit everyone. And we wanted to help to actually realize that technology and bring it to the world and democratize it. And we spent a long time just making progress where year over year the benchmarks would look better and better. But the first time we had something that was so useful that many people around the world wanted to use it was Chat GPT. And we were just blown away by the creativity and the ways in which people found how to really leverage the models we had produced in their daily lives. And so just out of curiosity, how many people in the room are Chat GPT users? That's pretty much the whole room. I would say I'm glad to hear it. But very importantly, how many of you have had an experience that was very key to your life or the life of a loved one, whether it's in healthcare, in helping manage a newborn, in any other walk of your life. And to me, that's the metric that we want to optimize. And seeing that number go up and to the right has been something that has been really different over 2025, right? That we really move from being just a text box that you ask a question, you get an answer, something very simple and contained, to people really using it for very personal, very important things in their lives. And it's not just in personal lives for healthcare and aspects like that. It's also in the enterprise, right? And really starting to bring models like Codex to be able to transform software engineering. And I think that this year we're really going to see enterprise agents really take off. We're seeing scientific discovery start to be really accelerated. Whether it's developing novel math proofs. The first time we saw that was just a couple months ago and the progress is continuing and it's really across every single endeavor of human knowledge work where there's human intelligence that can be leveraged, right? You can amplify it. We now have an assistant, right? We now have a tool, we have an adviser that is able to amplify what people want to do.
I completely agree with you, Greg. I think we have seen just an enormous acceleration of what we're using this tech for. Now, I would say I think every single time I see you, you tell me you need more compute.
It's almost like a broken record like you could just play the Greg wants more compute. Can you talk about just some of the things that you're seeing in the infrastructure, some of the bottlenecks and where do you think we should be focusing as an industry?
Well, the why we need more compute is the most important question, right? Which is really when the models are not that capable, right? Where we were in 2015, 2016, 2017, so forth, is that you basically just want to train a model and evaluate it, right? Maybe there'd be a very narrow task it'd be useful for. But as we've made this exponential progress on the models, then there's actually exponential utility to them. People want to bring it into their lives in a very scalable way. And I think that what we're seeing is as we move from you ask a question, you get an answer to agentic workflows where you ask the model to write some software for you and it goes off for minutes or hours or soon even days and you're not just operating one agent, you're operating a fleet of agents, right? You can have 10 different work streams all going at once on your behalf for a single developer, right? And that it should be the case that you wake up in the morning and this is the kind of thing we are going to build by the way. And Chat GPT has taken items off your to-do list at home and at work. And all of that is going to require that big graph that you had of how much compute the world's going to need. That's going to require far more compute than we have right now. Like I would love to have a GPU running in the background for every single person in the world because I think it can deliver value for them, but that's billions of GPUs. No one has a plan to build that kind of scale. And so what we're really seeing is benefits in people's lives, right? We're seeing for example some of my favorite applications and some of the ones I think are the most important are in healthcare right that we actually see people's lives being saved through Chat GPT. Just over the holidays, one of my co-workers, her husband had leg pain. They went to the hospital, they went to the ER and they got it x-rayed and the doctors are like, 'Ah, it's a pulled muscle, just wait it out, you'll be fine.' They went home, it got a little bit worse. Type the symptoms into Chat GPT, said go back to the ER, this could be a blood clot. And in fact, it was. It was deep vein thrombosis in the leg in addition to two blood clots on the lungs. And if they just waited it out, that would have been likely fatal. And it's not a unique story. Fiji, our CEO of applications I work very closely with every day. Chat GPT literally saved her life too, right? She was in the hospital for a kidney stone and had an infection. They're about to inject an antibiotic and she said, 'Wait just a moment.' She asked Chat GPT whether that one was safe for her. Chat GPT, which has all of her medical history, said no because you had this other infection two years ago that could re-trigger it and that could actually be life-threatening as well. And so she showed it to the doctor. The doctor's like, 'Wait, what? You had this condition? I didn't know. I only had five minutes to review your medical history.'
I completely agree, Greg. I mean that's one of the things that we can always all of us can use a helper. Yes.
And that's really what we have here.
Look, I mean I think you've painted a vivid picture of why we need more compute, of what we can do with AI. I think we feel exactly the same way. Now we've also done an incredible amount of work with your engineering teams. MI455 and Helios is actually a lot of it is through some of the feedback from our engineering teams working closely together. You know, can you talk a little bit about that infrastructure and what are your customers wanting and how are you going to use MI455?
Well, one of the key things with how AI is evolving is thinking about the balance of different resources on the GPU. And so we have a slide to show how we've seen the evolution of the balance of resources across different MI generations. So you see this slide that I very painstakingly put together. Actually, I did not painstakingly put it together. I asked Chat GPT to go put it to go create the slide. And so it literally did all the research and you can see some sources at the bottom and it actually went and read a bunch of different AMD materials, created these charts, put together the title, put together all these headers and produced not just an answer for me to then go do a bunch of work. It produced an artifact that I can show. And this is just one simple example that you can do today with Chat GPT, right? That we are moving to a world where you are going to be able to have an agent that does all this work for you. And for that, we're going to need to have hardware that is really tuned to our applications. What we have in mind is that we're moving to a world where human attention, human intent becomes the most precious resource. And so that there should be very low latency interaction anytime a human's involved. But there should be an ocean of agentic compute that's constantly running that's very high throughput and these two different regimes of low latency and high throughput yield a bunch of different pressures on hardware manufacturers such as yourself. So it's a pleasure to be working together.
We like building GPUs for you. That works well. Look, lastly Greg, let's talk a little bit about the future. Paint a picture. One of the things that we've talked about is there's some people out there who are wondering, is the demand really there? Can AI compute, do we really need all of this AI compute? And I know you and I have talked about it. I think people don't have a view of the future that you see. I mean, you have like a special seed. So, paint the world for what this looks like in a few years.
Well, looking backwards, we have been tripling our compute every single year for the past couple years and we've also tripled our revenue. And the thing that we find within OpenAI is every time we want to release a new feature, we want to produce a new model, we want to bring this technology to the world, we have a big fight internally over compute because there are so many things we want to launch and produce for all of you that we simply cannot because we are compute constrained. And I think we're moving to a world where GDP growth will itself be driven by the amount of compute that is available in a particular country in a particular region. And I think that we're starting to see the first inklings of this. And I think over the next couple years, we'll see it start to hit in a real way. And I think that AI is something where I think data centers can actually be very beneficial to local communities. I think that's a really important thing for us to really prove to people. But also the AI technology you produce, that is also something where in terms of scientific advances, you think about what has been the most fundamental driver of increase of quality of life, right? It really is about science. And every time we've gone into specific domains, you just see how much limitation there is from how things are done because there's a particular discipline that's built with a bunch of expertise. There are a small number of experts and it's hard for them to propagate that to future generations. For example, in biology, we hooked up GPT-5 to a wet lab setup and had humans describe what the wet lab looked like. The model said, 'Here are a couple ideas to try.' The humans would go try it and it actually produced a 79x, almost 100x fold improvement in the efficiency of a particular protocol. And that's just one particular reaction that people have spent some time actually optimizing but not like a ton of time because there's so much surface area available in biology that no human can possibly get to all of it. No human can be an expert across every single subfield. And I think what we're going to see is AIs that really bridge across disciplines that humanity has been unable to bridge. Right? You see this within healthcare where as humans learn more, we specialize more, but we're going to have AI that amplifies. And so I think it'll be for hard problems that AI will be brought to bear. This will be true for enterprise. Every single application I think will have an agent that is accelerating what people want to do. And I think the hardest problem for humanity will be deciding how do we use the limited resources we have to get the most benefit for everyone.
That is an incredible vision, Greg. We are so excited to be working with you. I think there's no question in the world that we have the power to really change people's lives. Thank you for the partnership and really look forward to it.
So, as you heard from Greg, compute is key and MI455 is a game changer. But with the MI400 series, we've designed a full portfolio of solutions for cloud, enterprise, supercomputing and sovereign AI. At the top is Helios that's built for the bleeding edge performance, hyperscale training, and distributed inference at rack scale. For enterprise AI deployments, we have Instinct MI440X GPUs that deliver leadership training and inference performance in a compact 8 GPU server designed for easy use in today's existing data center infrastructure. And for sovereign AI and supercomputing where extreme accuracy matters the most, we have the MI430X platform that delivers leadership hybrid computing capabilities for both high precision scientific and AI data types. This is something unique that we at AMD do because of our chiplet technology. We can actually have the right compute for the right application. Now, hardware is only part of the story. We believe an open ecosystem is essential to the future of AI. Time and time again, we've seen that innovation actually gets faster when the industry comes together and aligns around an open infrastructure and shared technology standards. And AMD is the only company delivering openness across the full stack. That's hardware, software, and the broader solutions ecosystem. Our software strategy starts with ROCm. ROCm is the industry's highest performance open software stack for AI. We have day zero support for the most widely used frameworks, tools, and model hubs. And it's also natively supported by the top open-source projects like PyTorch, vLLM, SGLang, Hugging Face, and others that are downloaded more than 100 million times a month and run out of the box on Instinct, making it easier than ever for developers to build, deploy, and scale on AMD. One of the exciting AI companies using AMD and ROCm to power their models is Luma AI. Please join me in welcoming the Luma AI CEO and co-founder Amed Jane to the stage.
Hello Amed. How are you? It's great to have you here with us. You're doing some incredible work in video generation and multimodal models. Can you tell us a little bit about Luma and what you're doing?
Absolutely. Lisa, thank you so much for having me here.
Luma's mission is to build multimodal and general intelligence. So AI can understand our world and help us simulate and improve it. Most AI video and image models today are in early stages and they're used to generate pixels. They're used to produce pretty pictures. What is needed in the world are more intelligent models that combine audio, video, language, image all together. So at Luma we are training these systems that simulate physics, causality, are able to go out, do research, call tools and then finally render out the results in audio, video, image, text, whatever is appropriate for the information that you're trying to work with. In short, we are modeling and generating worlds. So as an example, let me show you some results from our latest model Ray 3. By the way, Ray 3 is the world's first reasoning video model. So what it means is it actually is able to think first in pixels and latents and decide whether what it's about to generate is good. And it's also the world's first model that can generate in 4K and HDR. So, please take a look.
I could say that looked pretty incredible. So tell us how are customers using Ray 3 today? We are working with very large enterprises as well as individual creators across the spectrum and we work with them in advertising, media, entertainment and industries where you want to tell your story. 2025 was the year when they started to deploy our models and experiment with them and towards the end of it we saw large scale deployments where people are using our models for as much as actually making a 90 minute feature length movie. What customers are also asking us a lot for as they're using it more and more is control and precision. How can they get their particular vision out onto the screen and what we have realized through our research is that control comes from intelligence, not just better prompts. You can't keep typing in again and again to do those things. So we have built a whole new model on top of Ray 3 called Ray 3 Modify that allows you to edit the world. So, let me show you what that looks like. This won't have audio, and I'm going to tell you a little bit about what you're seeing. So, what's playing on the screen is a demo of Ray 3's world editing capabilities. It can take any real or AI footage, so footage from cameras or footage that you generated, and change it as little or as much as you want to realize the creative goals. It's a powerful system that we have developed for our most ambitious customers who are most demanding and they span the gamut across entertainment, advertising, and this has allowed us to enable a new era of hybrid human AI productions. The human becomes the prompt through motion, timing and direction, you act it out and then the model can produce it. What that means in practice is that filmmakers and creators can create entire cinematic universes now without elaborate sets and then edit and modify anything to get to the result they want. This has never been possible before. But in 2026, we are focused on going much further. 2026 will be the year of agents where AI will be able to help you to accomplish more of the task or hopefully the full end to end of the task rather than doing some patchwork. So our teams have been working diligently building the world's most powerful multimodal agent models. Using Luma will suddenly feel like you have a large team of capable creatives who are working with you in your creative pursuit. I want to show you a brief demo of what that would feel like. So what you're seeing here is a new multimodal agent that can take a whole script of ideas with characters and everything and start imagining that in front of you. Now this is not script to movie. This is human AI interaction and our next generation of models provide the ability to analyze multiple frames, long form video and make selection and maintain the fidelity of the characters, scenes and story and only editing when it's needed. Here you're seeing human and AI collaborate in designing characters, environment, shots and the whole world. And with our agents, we believe that creatives will be able to make entire stories that used to take a large production before. Again, this has never been possible before.
We've been using it heavily internally, and we couldn't be more excited. Individual creatives or small teams will suddenly have the power of doing what entire Hollywood studios do.
That's pretty amazing. It's really nice to see how these Luma agents come together and make this happen. Now, I know you have a lot of choices in compute, and when we first started talking, actually you called me and said you needed compute, and I said I thought I could help. Can you tell us a bit about why you chose AMD and what has your experience been?
Yeah, we bet on AMD very early on. That call was in 2024, early 2024. And since then, our partnership has grown into a large-scale collaboration between our teams. So much so that today 60% of Luma's rapidly growing inference workloads actually run on AMD cards. Today, we also... initially when we started out, we used to do a bunch of engineering, but today we are at a point where most any operators, most any workloads that we can imagine run out of the box on AMD. This is huge props to your software teams and the diligent work that is going into the ROCm ecosystem. We are building multi-modal models, and actually these workloads are very complex compared to text models. One example is these consume hundreds, thousands of times more tokens. A video that you saw, a 10-second video, is about 100,000 tokens easily. Compare that to a response from an LLM, it's about 200 to 300 tokens. So when we are working with this much information, TCO and inference economy is absolutely critical to our business, otherwise there's no way to serve all the demand that is coming our way. Through our collaboration with the AMD team, we have been able to achieve some of the best TCO — total cost of ownership — that we have ever seen in our stack. We believe as we build these more complex models that are able to do autoregressive diffusion and text, image, audio, and video all at the same time, this collaboration will allow us to significantly differentiate on cost and efficiency, which as you know in AI is a big deal. Through this collaboration, we have developed such a degree of confidence that in 2026 we are expanding our partnership to a tune of about 10 times what we have done before, and these MI455 cards... I cannot be more excited for MI455X because the rack-scale solution and the memory and the infrastructure that you're building is essential for us to be able to build these world simulation models.
Well, we love hearing that, Amit. And look, our goal is to deliver you more powerful hardware. Your goal is to make it do amazing things. So just give us a little brief view of what do you see customers doing over the next few years that just isn't even possible today?
Right. So as Greg was mentioning early on in LLM land, in 2022, 2023, they were great for writing copy, small emails, things like that. We could have never imagined that we would actually put these models into real-time systems, into healthcare and these kinds of things through accuracy and architecture and scaling. LLMs have now gotten to that point. Video models are currently in that early stage. Today they're great for generating video and pretty pictures. But soon, by scaling these models up, by improving the accuracy and data, we would end up at a place where these models will help us simulate real physical processes in the world, like CAD architecture, fluid flows, help us design entire rocket engines, plan cities. This is not outrageous. This is what we do today manually with big giant teams in simulation environments. These models will allow us to do that and automate that to a great degree. As they become more and more accurate, multi-modal models is what we need for the backbone of general purpose robotics. Your home robot will run hundreds of simulations in its head in image and video, and then try out how to do things, how to solve problems. So it is able to do a lot more than current generation of LLM and VLM robots are able to do. This is how the human brain works. Humans are natively multimodal. Our AI systems will be as well.
That sounds wonderful, Amit. Look, thank you so much for being here today. Thank you for the partnership, and we really look forward to all that you're going to do next.
So, you've heard from Greg and Amit. What they said is they need more compute to build and run their next-gen models. And it is the same across every single customer that we have, which is why the demand for compute is growing faster than ever. Meeting that demand means continuing to push the envelope on performance far beyond where we are today. MI400 series was a major inflection point in terms of delivering leadership training across all workloads, inference, scientific computing. But we are not stopping there. Development of our next-gen MI500 series is already well underway. With MI500, we take another major leap in performance. It's built on our next-gen CDNA 6 architecture, manufactured on 2nm process technology, and uses higher speed HBM4E memory. With the launch of MI500 in 2027, we're on track to deliver 1,000x increase in AI performance over the last four years, making more powerful AI accessible to all. So with that, let's thank you. So with that, now let's shift from the cloud to the devices that make AI more personal: PCs. So for decades, the PC has been about a powerful device helping us be more productive, whether at work or at school. But with AI, the PC has become not just a tool, but a powerful, essential part of our lives as an active partner. It learns how you work and adapts to your habits. And it can help you do things faster than you've ever expected, even when you're offline. AI PCs are starting to deliver real value across a wide range of everyday tasks, from content creation and productivity to intelligent personal assistance. Let's just take a look at a few of the AI PC applications today. Starting with content creation. These videos were created from simple text prompts on a Ryzen AI Max PC. So, not in the cloud, but in a local environment. Anyone can generate professional quality photos and videos in minutes with no design expertise. Microsoft has been a key enabler of AI PCs, helping bring next-generation capabilities directly into our productivity tools. For example, managing your meetings, summarizing meetings, summarizing emails, quickly finding files that you need, using real-time translation on video conferences. And with Microsoft Copilot, advanced AI capabilities are being built directly into the Windows experience to complete tasks faster. You just describe what you need and the PC takes it from there. At AMD, we saw the AI PC wave early and we invested. That's why we led every inflection point. We were the first to integrate a dedicated on-chip AI engine in 2023 and the first to deliver Copilot Plus x86 PCs in 2024. And with Ryzen AI Max, we created the first single-chip x86 platform that could run a 200-billion parameter model locally. And now we're extending that leadership again with our next-gen Ryzen AI notebook and desktop processors. Today, I am proud to announce the new Ryzen AI 400 series, the industry's broadest and most advanced family of AI PC processors. Ryzen AI 400 combines up to 12 high-performance Zen 5 CPU cores, 16 RDNA 3.5 GPU cores, our latest XDNA2 NPU delivering up to 60 TOPS of AI compute, and support for faster memory speeds. These flagship Ryzen AI mobile processors deliver significantly faster content creation and multitasking performance compared to the competition. There's a lot of excitement for the Ryzen AI 400 series, and if you're walking around CES this week, you're going to see many notebooks launching this week. The first Ryzen AI 400 series PCs begin shipping later this month with more than 120 ultra-thin gaming and commercial PCs launching throughout the year from every major OEM across every AI PC form factor. Now, powering the next generation of AI PC experiences takes more than just hardware. It takes smarter software with models that are lighter, faster, and can run directly on device. These are different than what you're seeing in the cloud. So, to talk more about this next wave of model innovation, please welcome Ramine Hassani, co-founder and CEO of Liquid AI. Rainine, it's great to have you here. I'm very excited about the work that you guys are doing at Liquid. You are really taking a different approach to models. Can you talk a little bit to the audience about what Liquid is doing and why it's different from others?
Absolutely, Lisa. It's great to be here. We are a foundation model company spun out of MIT two and a half years ago. We're building efficient generative AI models that can run fast on any processor inside and outside of data centers. We designed from scratch multimodal models with a hardware-in-the-loop approach that allows us to optimize neural architectures for a given hardware. We are not building transformer models. We are building liquid foundation models: powerful, fast, and processor-optimized generative models. The goal is to substantially reduce the computational cost of intelligence from first principles without sacrificing quality. That means liquid models deliver frontier model quality right on a device. Device could be a phone, a laptop, a robot, a coffee machine, or an airplane. Basically anywhere compute exists, with three value propositions: privacy, speed, and continuity. It can work seamlessly across online and offline workloads.
Ramine, you know our teams have been working really closely on bringing more capable models to AI PCs. Can you share a bit about that work?
Absolutely. Today I've got two new product announcements. One, we are excited to announce Liquid Foundation Models 2.5, the most advanced tiny class of models on the market. At only 1.2 billion parameters, the model performs best on instruction-following capabilities between its class and models that are larger in its class. LFM 2.5 instances are the building blocks of reliable AI agents on any device. To put this in perspective for you, this model delivers instruction-following capabilities better than the DeepSeek models and Gemini Pro models right on the device. We're releasing five model instances: a chat model, an instruct model, a Japanese-enhanced language model, a vision language model, and a lightweight audio language model. These are highly optimized for AMD Ryzen AI CPUs, GPUs, and NPUs. Today they're available for download on Hugging Face and on our own platform, Leap. You can enjoy them.
So, we can stack these LFM 2.5 instances together to build workflows. But then it would be really amazing if we can bring in all these modalities into one place. That brings me to my second announcement: LFM 3. It is designed natively multimodal to process text, vision, and audio as input and deliver audio and text as output in 10 different languages, with sub-millisecond latency for audio-visual data. You will get LFM 3 later in the year.
All right. That's fantastic. So now, Ramine, help our audience understand why they should be so excited about LFM 3? Like what can we do with these models on an AI PC?
Absolutely. Most AI assistants today are reactive agents. You open an app, ask a question, it responds. But when the AI is running fast on the device and is always on, it can be working on tasks proactively for you. Tasks can be done in the background. Let me show you a quick demo, a reference design to inspire what is possible to build on PCs with LFM instances. Let's jump in. Imagine you're a sales leader working on your AMD Ryzen laptop with an LFM 3 backbone proactive agents activated. You're in full focus mode working on a spreadsheet. Notifications start piling up. You get a calendar notification for a sales meeting but want to continue your work in focus. The Liquid proactive agent notices the meeting and offers to join on your behalf. You allow the agent to join and while you focus on your data analysis task, in the background the meeting is in progress with your agent representing you. Are you sure we can trust this agent? I think... I'm a little worried there, Ramine. But this system can actually transcribe more than transcribing, truly understanding what is going on. The system can also be hooked up to your email platform; it can analyze your emails as they come in and perform deep research functionality. With that deep research functionality, you can analyze every email and draft the response for you, again everything under your own control. It's not going to go rogue; everything is offline locally on the device. So this system can deliver a summary and can do the jobs better than what you have seen from reactive agents. I think this year is going to be the year of proactive agents, and I'm very excited to announce that we are collaborating with Zoom to bring these features to the Zoom platform.
That's fantastic, Ramine. We're really excited about what you're doing. I think you've given people a glimpse of what we can do when we bring true AI capability to our PCs. Thank you. We're excited and we look forward to all we're going to do together.
Thank you so much. Thank you for having me.
So, now you've seen a little bit about what's possible with local AI, but the latest PCs aren't just running AI apps. They're actually building them. That's why we created Ryzen AI Max, the ultimate PC processor for creators, gamers, and AI developers. It's the most powerful AI PC platform in the world with 16 high-performance Zen 5 CPU cores, 40 RDNA 3.5 GPU compute units, and an XDNA2 NPU delivering up to 50 TOPS of AI performance, all connected by a unified memory architecture that supports up to 128 gigabytes of shared memory between the CPU and GPU. In premium laptops, Ryzen AI Max is significantly faster in both AI and content creation applications compared to the latest MacBook Pro. In small form factor workstations, Ryzen AI Max delivers comparable performance at a much lower price than Nvidia's DGX Spark, generating up to 1.7 times more tokens per second per dollar when running the latest GPT models. And because Ryzen AI Max supports both Windows and Linux natively, developers maintain full access to their preferred software environment, tools, and workflows. There are more than 30 Ryzen AI Max systems in market today with new laptops, all-in-ones, and compact workstations launching at CES and rolling out throughout the year. But our mission is to advance AI everywhere for everyone. The truth is there are AI developers, many of you in this room, who want access to platforms that enable you to develop on the fly. So we took this one step further. Today, I am excited to announce the AMD Ryzen AI Halo, a new reference platform for local AI deployment. I would say this is pretty beautiful. Do you guys agree? So let me tell you what it is. This is the smallest AI development system in the world, capable of running models with up to 200 billion parameters locally, not connected to anything. It's powered by our highest-end Ryzen AI Max processor with 128 GB of high-speed unified memory shared by CPU, GPU, and NPU. This architecture accelerates system performance and makes it possible to efficiently run large AI models on a compact desktop PC that fits in your hand. Halo supports multiple operating systems natively, ships with our latest ROCm software stack, comes preloaded with leading open-source developer tools, and runs hundreds of models out of the box. This gives developers everything you need to build, test, and deploy local agents and AI applications directly on the PC. For all of you wondering, Halo is launching in the second quarter of this year, and we can't wait for folks to get their hands on them.
Which makes all of us unique. And therefore it's essential to be able to measure that to prevent, diagnose, and treat diseases. In a simplified way, you can think about the human genome as three billion letters. So that actually is like a book with 200,000 pages, and that is in each of our cells. Now, if there's just one spelling mistake in that book, that can actually mean the difference between a long and healthy life and a short and terrible life. So accurate DNA sequencing is extremely important, but is super data and computer intensive. In fact, we are generating on our sequencers more data than is generated on YouTube every day. And therefore, the relationship with AMD is super important. We are using your FPGA and EPYC processors in our sequencers every day. And that's the only way we can compute all that and translate that into insights.
Over the past decade, our technology has already now been used, as we talked about, in drug discovery, but also impacting healthcare. Today it's used for profiling terrible diseases like cancer and inherited diseases, and it's really very important to make sure we are impacting a lot of people's health out there and have saved millions of people's lives. But we're just getting started. Biology is super complex, so our brain can't really comprehend all that, but the combination of using generative AI, genome, and proteomics together is poised to completely change our understanding of biology over the next period of time. It will impact drug discovery, but it will also impact how we prevent and treat early diseases. So really it will change our way we think about longevity and healthier lives, and we can only do that with the collaboration between us and all of us on the stage and the whole ecosystem. So I'm really excited about that.
That's fantastic. Jacob and Ola from AstraZeneca, you're scaling AI across one of the largest drug discovery pipelines there is. Talk about how AI is changing the way you develop new medicines.
Okay, thanks Lisa and also thanks for the invitation. So at AstraZeneca, we really apply AI end to end from early drug discovery to manufacturing to healthcare delivery. And for us, AI is not only about productivity, it's a lot about innovation. How can we work in a different way? How can we do new things with AI? And one area that I'm personally very passionate about is how can we deliver candidate drugs quicker with the help of generative AI. So how we're working there is that we train our generative AI model on all our experimental data that we have generated over several decades, and then we use those models to virtually assess in the computer which hypothesis, which IDs of candidate drugs might work or not. And then we can assess millions of different potential candidate drugs, and then we take the best, only the ones that we think are really good, into the experimental lab and really validate the hypothesis there. So we use a generative AI model to generate candidate drugs, to modify them, to optimize, to really reduce the number of experiments we need to do in the laboratory. And we are applying this new way of working through the whole AstraZeneca small molecule pipeline, and we see that we can deliver candidate drugs 50% faster with the new way of working and also improve clinical success later. And we can't do that alone. We need to do that in collaboration. So we collaborate with academia, with AI startups, and with companies like AMD. And one very important area for us is hyperscaling, because we have a lot of great data and we really want to create the most optimal best models we can. And there we work in a collaboration with AMD to basically scale our drug discovery engine so it can handle this large new data set. So basically we optimize the whole workflow with the help of AMD.
That's fantastic, Ola. Look, all of your stories are really amazing, and we're thrilled to be working with you to bring these things to life. Now, let's wrap up and think about what's the one thing each of you are most excited about when it comes to how AI will improve healthcare. And maybe Jacob, we'll start with you.
Yeah, I'm just excited about the time we're in. This is the first time that you have technology that can create massive amounts of data, the first time you have the compute power and the generative AI models that will truly change our understanding of biology, as I mentioned before, that will be translated into huge impact on healthcare.
