At CES 2026, innovators from every corner of the globe show up to the most powerful tech event in the world. We'll rally together, imagining what's next. And we'll make it real. Solving the greatest challenges facing our world and proving that technology isn't just moving us forward. It's creating new possibilities. Here, AI isn't an idea. It's intelligence in motion. The innovators, the storytellers, and the game changers come together to redefine the landscape of content, creativity, and culture. Pioneers will push healthcare beyond what we ever thought possible. From connected roads to autonomous flight, from next-gen marine tech to innovations that feed the planet. This is what drives us forward. In the halls of CES, technology doesn't just compute, it collaborates. Quantum thinkers, cybersecurity leaders, fintech visionaries, and robotics pioneers. CES 2026 innovators show up.
CEO of the Consumer Technology Association, Gary Shapiro.
Thank you very much. Good evening, everyone. Welcome to Las Vegas, CES 2026, with our first keynote of the year.
Every year, CES gives us a front row seat to the ideas and breakthroughs that shape the next decade. But it's actually the people behind those breakthroughs, the leaders challenging what's possible, who truly define the moment. Tonight we get a head start to CES 2026 with one such leader. You know, CES has always been a platform for bold vision and ambition. It's where ideas become industries and where the next era of human progress first takes shape. And this year, as AI accelerates change across every sector, it's more important than ever that we hear directly from the architects building the systems and breakthroughs that will define our very future. And that's why it is a tremendous honor to introduce to you a leader, quite frankly, one of the bastions of CES whose vision and impact can be felt across the entire technology landscape. Dr. Lisa Su. Lisa is no stranger to this stage. She has keynoted CES before, and each time she's done so, she's helped set the tone for the industry and the year ahead. Since becoming CEO in 2014, Lisa has led the company through one of the most remarkable transformations in modern technology. Under her leadership, AMD reinvented itself through relentless innovation in high-performance and AI computing, delivering products that now power AI training and inference, scientific research, enterprise workloads, cloud infrastructure, and the devices and experiences which millions of people rely on every single day. Today, AMD is a central force in the global AI transformation. Its CPUs, GPUs, and adaptive computing solutions help unlock new capabilities across cloud, enterprise, edge, and PC. And while the industry has spent years discussing what AI could become, Lisa has been focused on building the computing foundation that makes AI real and accessible at scale. But what truly distinguishes Lisa is her leadership. She's analytical and deeply technical, yet always grounded in purpose. She brings a rare combination of scientific brilliance, strategic clarity, and human-centered thinking. She believes in partnering deeply to engineer solutions that matter, solutions that advance society, strengthen industries, and expand opportunity. That's the kind of leadership CES is designed to elevate, and that's the kind of leadership we all need as we navigate a world of accelerated innovation. Tonight, Lisa will share AMD's vision for how high-performance computing and advanced AI architectures will transform every part of our digital and physical world, from research, healthcare, and space exploration to education and productivity. She'll speak to the extraordinary pace of AI, the breakthroughs happening now, including the opportunities and responsibilities that come with building the future.
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 up. 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 Su.
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 ChatGPT 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 100 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. You know, 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. So 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 ChatGPT or Gemini or Grok or you're coding with copilots, 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 yottascale 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 yottascale 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 look 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 4,600 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 to 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 ChatGPT 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. ChatGPT is very much the overnight success that was seven years in the making, right? That 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 ChatGPT. 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 ChatGPT 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 that seeing that number go up and to the right has been something that has been really different over 2025, right? That we really moved from being just a text box that you ask a question, you get an answer, something very simple 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? That 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, you know, 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 you know, where do you think we should be focusing as an industry?
Well, the why do we need more compute is the most important question, right? Which is really when the models are not that capable, right? And 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 ChatGPT has taken items off your to-do list at home and at work and that all of that that's going to require that you know 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 ChatGPT just over the holidays one of my co-workers that 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 ChatGPT said go back to the ER this could be a blood clot. And in fact, 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. ChatGPT 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 ChatGPT whether that one was safe for her. ChatGPT which has all of her medical history said, "No, 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 this 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 ChatGPT 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 an artifact that I can show. And this is just one simple example that you can do today with ChatGPT 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. You know paint a picture. You know, one of the things that we've talked about is there's some people out there who are wondering, you know, is the demand really there? Can AI compute like 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 seat. 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-bound. 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 that's just, you know, there's a particular discipline that's built with a bunch of expertise. There are small number of experts and that 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, you know, 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 and ton of time because it's just 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 is going to amplify. 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, HuggingFace, and others that are downloaded more than a hundred 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 Amit Jain to the stage.
Hello Amit, 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. Of course. 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, they're in early stages and they're used to generate pixels. They're used to produce, you know, 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 latency 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.
When you close your eyes, what do you see? When we talk about reality, how do you know what is real? Or what is simply your imagination? Now open your eyes.
I could say Amit that looked pretty incredible. So tell us how are customers using Ray 3 today?
So 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 like you know 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're seeing 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 and actually 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 actually 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 — 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 spread 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 actually going much further. 2026 will be the year of agents, where AI will be able to help you 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 selections while maintaining 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, environments, 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, and we have 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 — so you know, 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, and this is huge props to your software teams and the diligent work that is going into the ROCm ecosystem. We are building multimodal models, and actually these workloads are very complex compared to text models. One example of that is these consume hundreds of times, thousands of times more tokens. A video that you saw, a 10-second video, it's 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 TCOs — total cost of ownership — that we have ever seen in our stack. And we believe as we build these more complex models that are able to do autoregressive diffusion and that are able to do text and image and 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. So 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 — [applause] — and I cannot be more excited for MI455X because this 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, right, like you know, 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 are 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. And 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. And as they become more and more accurate, multimodal models are 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, like, okay, how do I do this? How do I solve this? So that it is able to do a lot more than the 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.
Thank you.
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. Now 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 CDNA6 architecture, manufactured on 2nm process technology, and uses higher-speed HBM4E memory. And with the launch of MI500 in 2027, we're on track to deliver a 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. [applause] 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 it 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. Now, at AMD, we saw the AI PC wave early and we invested. That's why we've 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. So today, I'm 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. Now, 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. [applause] 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. Ramine, it's great to have you here. I'm very excited about the work that you guys are doing at Liquid. You were 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 is 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're 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. The device could be a phone, could be a laptop, could be a robot, could be a coffee machine, and could be 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 kind of models, Gemini 2.5 Pro, 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. Basically, these are highly optimized for AMD Ryzen AI CPUs, GPUs, and NPUs. And today they're available for download on Hugging Face and on our own platform, Leap. You can enjoy them.
That's pretty cool. [applause]
So, we can stack these LFM 2.5 instances together to build agentic workflows. But then it would be really amazing if we can bring in all these modalities into one place. So that brings me to my second announcement: LFM 3. LFM 3 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 3 later in the year.
All right. That's fantastic. So [applause] now, Ramine, help our audience understand, like, why should they be so excited about LFM 3? Like, what can we do with these models on an AI PC?
Absolutely. So most AI assistants, copilots today are reactive agents. You open an app, then you ask a question, it responds. But when the AI is running fast on the device and is always on, it can be working on the tasks proactively for you. The tasks can be done in the background. So 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 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 deep focus. A 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 [laughter] I'm a little worried there, Ramine.
This system can actually transcribe more than transcribing your systems, you know, and really understanding what is going on. And the system can also be hooked up to your email platform. It can analyze your emails as your emails are actually coming in. It can perform a deep research functionality. So with the deep research functionality, you can analyze every email and draft the response for you. Again, everything under your own control. You know, you're not going to go — this is not going to go rogue. Everything is offline, locally on the device. So this system can deliver, you know, a summary and can do the jobs better than what you have expected, what you have seen from reactive agents. I think this year is going to be the year of, you know, proactive agents, and I'm very excited to announce that we are bringing — we're working, we're collaborating with Zoom to bring these features to the Zoom platform, actually.
That's fantastic, Ramine. [applause] We're really excited about what you're doing. I think you've just given people just a glimpse of what we can do when we bring, you know, true AI capability to our PCs. So, 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. Thank you.
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 and 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 GB 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-OSS models. And because Ryzen AI Max supports both Windows and Linux natively, developers maintain full access to their preferred software environment, tools, and workflows. Now, 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'm excited to announce the AMD Ryzen AI Halo, a new reference platform for local AI deployment. [applause] Now, I would say this is pretty beautiful. Do you guys agree? [applause] 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 that is 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. Thank you. [applause] Halo supports multiple operating systems natively, ships with our latest ROCm software stack, comes preloaded with the leading open-source developer tools, and runs hundreds of models out of the box. And this really gives developers everything you need to build, test, and deploy local agents and AI applications directly on the PC. Now, for all of you who are wondering, Halo is launching in the second quarter of this year, and we can't wait for folks to get their hands on them. [applause] So, now let's turn to the world of gaming and content creation. A few gamers out there? I think there are a lot of gamers out there. Look, every day gamers and creators rely on AMD across Ryzen and Radeon PCs, Threadripper workstations, and consoles from Sony and Microsoft to deliver tens of billions of frames. And while the visual quality of those frames has advanced dramatically over the years, the way we build those worlds really hasn't. It still takes teams months or even years to bring a 3D experience to life. Now, AI is really starting to change that. To show what's next in 3D world creation, I'm honored to introduce one of the most influential figures in AI. Known as the godmother of AI, her work has transformed how machines see and understand the world. Please welcome the co-founder and CEO of World Labs, Dr. Fei-Fei Li. [music] Fei-Fei, we are so excited to have you here. You know, you've been one of the leaders shaping AI for decades. Can you just give us a little bit of your perspective? Where are we today and why did you start World Labs?
Yeah, first of all, thank you, Lisa, for inviting me to be here. Congratulations to all the announcements. I can't wait to use some of them. So it's true that there has truly been great breakthroughs in AI progress in the past few years, and as you said, I've been around the block for a while, for more than two decades, and I really cannot be more excited than now by where things are going. So in the past few years, language-based intelligence in AI technology really has taken the world by storm. We're seeing the proliferation of all kinds of capabilities and applications. But the truth is there's a lot more than just language intelligence. Even for us humans, there's more than passively looking at life in the world. We are incredible spatially intelligent animals. And we have profound capabilities that use our own spatial intelligence that connects perception with action. Think about all of you being here. How you braved through airports this morning — I'm one of them — or woke up in your hotel room and got to the nice coffee shop, or found your way in this maze in Vegas to be here. All this requires spatial intelligence. So what excites me is that there's now a new wave of Gen AI technology, both embodied AI and generative AI, that we can finally give machines something closer to human-level spatial intelligence. It's the ability to not only perceive but create 3D or even 4D worlds, reason about objects and people, and imagine entirely new environments that still obey the laws of physics and dynamics in worlds, virtual or real. So that's why I started World Labs. I really want to bring spatial intelligence to life and deliver value to people.
I remember the first time I talked to you about your concept for World Labs and your passion about what this could bring. Tell us a little bit about what your models do so the audience gets a feel for what does this really mean.
Yeah. Well, I heard that there are gamers out there. So this is very exciting. So who traditionally building 3D scenes requires laser scanners or calibrated cameras or hand-built models using pretty sophisticated and complicated software. But at World Labs, we're creating a new generation of models that can use the recent Gen AI technology to learn the structure — not only just flat pixel structure, I'm talking about 3D, 4D structure of the world — directly from data, a lot of data. So give the model a few images, and even one image, the model itself can fill in the missing details, predict what's behind objects, and generate rich, consistent, permanent, navigable 3D worlds. So what you're seeing here on screen is a hobbit world that's created by our World Labs model called Marble. We just gave it a handful of images and it created these 3D scenes that are persistent and you can navigate. You can even see a top view, and our system transformed a few visual inputs into a fully navigable, expansive 3D world. And it shows how these models not just reconstruct environments — they really imagine cohesive worlds, wondrous worlds. And once these worlds exist, they flow together, allowing effortless transition from one environment to the next and scaling into something much larger. And this is much closer to how humans piece together a place from a few glances.
Well, it looks pretty amazing that you can do that with such little input. Now, can you just show us a little bit about how the technology works?
Yes, definitely. Let's just ground it in a real world a bit more from the hobbit world, and let's do something that you're very familiar with. So over the break, our team went to AMD's Silicon Valley office. I hope they got your permission and [laughter] they did not, but that's okay. Okay. Well, now here we are. [laughter] We just used some regular phone cameras. There's no special equipment, just phones to capture a few images, and then we put them into World Labs' generative 3D model called Marble. And then our model that can use AMD's MI325X chip and the ROCm software stack can create a 3D version of that environment, including windows, doors, furniture, size, and sense of depth and scale. And keep in mind, you're not looking at photos, you're not looking at videos, you're looking at truly 3D-consistent worlds. And then our team started to have a little more fun and decided to — you decided to remodel. Exactly. For free for you [laughter] for different design styles, right? I don't know which one you guys like the most. I personally really like the Egyptian one, but maybe that's because I'm going there in a few months. And while this transformation is keeping the geometric consistency and the 3D inputs, so you can imagine this can be such powerful tools for many use cases, whether you're doing robotic simulation or game development or design. This can — what would traditionally take months to do in a typical workload, we really could do it in minutes now. And we can even navigate into an entirely different world, like actually the Venetian hotel. And we just did that yesterday by taking a picture and then putting it in the model, and then had a little fun, and then it turned this whole place into a 3D imaginative space. [applause] And now I'm sure you guys can take pictures and send it to Marble and experience this yourself. But what you don't see here behind the scenes is how much computation is happening and why inference speed really matters. The faster we can run these models, the more responsive the world becomes. Instant camera moves, instant edits, a scene that stays coherent as you actually navigate and explore. And that's what's really important, you know.
Fei-Fei, I think you're going to have a few people going out to your website to try Marble this year.
We'll keep the server up. [laughter]
But look, that looked really amazing. Can you just share a little bit about your experience working with AMD and our work on Instinct and ROCm?
Yeah, of course. And even though we are old friends, our partnership is relatively new. And I got to be honest, I'm very impressed by how quickly this came together. Part of our model is a real-time frame generative model. It was running on MI325X in under just a week. And with AMD Instinct and ROCm, our teams were able to iterate really rapidly over a course of a few weeks to improve performance by more than fourfold. And that was really impressive. That matters because spatial intelligence is fundamentally different from what came before. Teaching AI to understand and navigate 3D structure, have motion, understand physics requires enormous memory, massive parallelism, and very fast inference. And I was seeing your announcement. I can't wait to see platforms like MI450 continue to scale, and they will give us the ability to train larger world models and, just as importantly, to run them fast enough that these environments can feel alive, react instantly as the user or agents move, explore, interact, and create.
Now that's wonderful. Thank you for those comments. Your team has been fantastic working together. So, Fei-Fei, with all the compute performance that we're going to give you and all of the innovation in your models, give the audience a view of what to expect over the next few years.
Yeah, I know. And as you know me, I don't like to hype. I think the world — this is like called underhype. So [laughter and gasps] no, I think we should just share what it is. It is going to be a changing world. A lot of workflows, a lot of things that were difficult to do will actually go through a revolution because of the incredible technology. So for example, creators can now experience and create real-world scenes in real time, shaping what's in their mind's eye, experimenting with the space, the light, the movement, as if they are sketching inside a living world. And then intelligent agents, whether it's robots or vehicles or even tools, can learn inside very rich physics-aware digital worlds before they even need to be deployed into the real one, making them much safer, making the development much faster, more capable, and more helpful to people. And designers, for example, architects can walk through ideas before anything is built, exploring form, flow, materials, and navigate spaces rather than just staring at static plans. So what excites me most is that this represents a shift in how AI shows up in our lives. We're moving from systems that understand words and images passively to systems that not only understand but can help us to interact with the world. So Lisa, what we are sharing today — which is turning a handful of images or photos into coherent, explorable worlds in real time — is not a glimpse of the distant future anymore. It is really the beginning of the next chapter. So you and I talk about this even offline. We know as powerful as AI technology is, it's also our responsibility to deploy and develop it in ways that reflect true human values, that augment human creativity, productivity, and our care for each other, while keeping people firmly at the center of this story, however powerful technologies are. And I'm very excited to partner with AMD and with you on this journey.
I think I speak for everyone. You are really an inspiration to the AI world. Congratulations on all the great progress, and thank you for joining us tonight.
Thank you, Lisa. [applause] [music]
Okay, next shift. Now let's turn to the world of healthcare. [music] Of all the ways AI is advancing the world, healthcare impacts us all. And AMD technology is enabling the incredible to become possible. Cancer detection is happening earlier with supercomputers analyzing data at massive scale. Patients are receiving therapies sooner with compute modeling complex biological systems. Promising treatments are moving forward faster through molecular simulation. Medicine is becoming more personalized through genome research, and patient outcomes are improving through robot-assisted surgeries. Our partners are using AI to accelerate science and better human health. Advanced by AMD. [applause] So look, as you saw in that video, AMD technology is already at work across healthcare. This is one of the most meaningful applications. You've already heard about some of the stories tonight of how high-performance computing and AI is one of the areas that I am most personally passionate about — is how you can bring healthcare there. There's nothing more important in our lives than our health and the health of our loved ones. And using technology to improve healthcare outcomes means we measure progress in terms of lives saved. I'm very happy to be joined tonight by three experts who are leading the way in applying AI to real-world healthcare challenges. Please join me in welcoming Sean Mlan, CEO of Absci, Jacob Than, CEO of Aluminina, and Ula Enis, head of molecular AI at AstraZeneca. How are you? How are you? How are you? How are you? All right, guys. Thank you so much for being here. You can see there's a lot of excitement about healthcare. Thank you for the tremendous partnership. You know, Sean, at Absci, you're using generative models and synthetic biology to design new drugs from scratch. Can you walk through a little bit of how that works?
Yeah, thank you so much for having us here today. Biology is hard. It's complex. It's messy. Drug discovery and development is this archaic way of going about discovering drugs. Ultimately, it's this trial-and-error process where you ultimately are searching for a needle in the haystack. But with generative AI and what we're doing at Absci, you're actually able to start creating that needle and being able to actually engineer in the biology that you want, being able to go after the diseases that have large unmet medical need and being able to have the manufacturability, the developability that you want in the drug. We're actually able to start having precision engineering now because of AI with biology. Just like Apple is engineering an iPhone or you all are engineering the 455s, we're able to start engineering biology. And what is that actually doing? It's allowing us to start tackling some of the hardest, most challenging diseases that still exist that have high unmet medical need where standard of care is poor. And at Absci, this is exactly what we want to tackle. We want to tackle these hard, challenging diseases. Two of them that we're focused on at Absci is androgenic alopecia. So think common baldness. We actually have the opportunity in the not-too-distant future to have AI cure baldness. Wouldn't that be incredible? [laughter] [applause] And not only that, be able to focus in areas that have been neglected. Women's health for far too long. Women's health has been pushed aside, and we have a drug that we are developing for endometriosis that affects one in 10 women, with the opportunity to potentially deliver a disease-modifying therapy for these women. This is what AI and drug discovery is all about. [applause] And this wouldn't be possible without the compute partnership that we've had with AMD. Lisa, you and Mark Papermaster invested in Absci roughly a year ago. And within that year, we've been able to —
