Advancing AI 2026 | Build What's Next with @AMD

The next wave of AI is here. Evolving beyond thinking to acting. And it's bigger than anyone imagined. Except AMD. We recognize this agentic era would require the highest performing GPUs and CPUs working together. So we're the only ones who build both. It's how agentic AI moves from answers to action. Pushing inference compute to tremendous scale. Like an agent that frees thousands of employees from smaller tasks to focus on bigger things. A car that safely handles a new city like a local on its first day there. And an agent that pinpoints a disease in millions of cells, then designs a cure. We've built more AI computing power into a single data center than existed on Earth a decade ago. And now, we're giving you the tools. So together, we advance AI.

We did, yeah.

Yeah. Yeah. And first, thank you so much. A hard act to follow with Helios.

So with Anthropic, as you mentioned, the scale that we're growing just as an industry is enormous. And so we have been working to make sure that we have the best, like different chips, and we can use the best chips for the best workloads.

Yeah. So I think that the first thing was just Helios is an amazing machine. It's absolutely fantastic. Do we like that? Does that work for us? And then I think the thing that we were thinking about originally was whenever we're bringing up a new hardware platform, it's a big effort. It's like a huge thing. And so as part of the... as we were thinking about this, we started doing our own evaluation of MI355. You guys generously got us a rack to start working. And we expected this to be kind of a big process. Our actual experience was we had one engineer start doing it. They spun up... they connected it to Claude, asked him, hey, bring up this machine, left it going over the weekend. And we ended up with a graph of the actual performance of our leading model on it just going up and up and up over the weekend. And so, yeah. I think that that's a testament to the open platform that you guys have made where anyone like human or AI can now build real models on your platform.

Yeah, so I think this is a great place for collaboration where the bread and butter of Claude is doing software engineering. And now more and more we're seeing it help out with the type of workloads that you guys are doing also, like design, layout, which is not quite the normal software engineering, but is adjacent. And I do think that that is the place where we can work together to make the next generation of chips even better.

Yeah, that's a great question. So I think the working together for the scale-up seems like it's probably the biggest thing that I'm most excited for, where it does seem like we see consistently using more better compute results in better models that then add more value to all the folks using it downstream. So I think that's the biggest thing. And then I think one thing now that we're investing more and more with you guys, too, and I think we can continue to, is security, where that's a place where now, as the machines are getting more complex, we can work together to secure things at the chip layer, the server level, the rack, the whole network, and that'll help out, like, not just us, but the entire industry stay safe.

Thank you.

Thanks, great to be here. I was thinking about this event over the last two years. It feels like the attendance at this event is following scaling laws. It's been doubling every year over the last two years. But more seriously, to your question, we've always predicted that models will evolve where they will start as chatbots, then they become reasoners, then they become agents, then they become interns. And the trajectory has kept up with that. So we are all seeing that models are becoming a lot more capable, more agentic, and able to do very long-running tasks. Let them go and they just take care of it. And so all that really means is we need to keep scaling compute. The more compute we scale and throw at training, the more capabilities emerge, the more compute we scale and provide it to distributing intelligence to the world, the more people want to do with it. And this is what we see with exploding token budgets. We've seen internally, for example, in OpenAI, that not just engineering, but every aspect of the enterprise is beginning to use agents for every part of the work. This is why we need platforms like yours that can scale with how quickly the capabilities of these models are scaling, as well as how quickly the whole world is beginning to embrace what these things can do.

No, it's been a phenomenal partnership. As you mentioned, we were first to bet on AMD, and we are really thrilled with how that bet is turning out for us. It got started on MI300 and expanded very quickly to 355. And it's been really fun for our team to see how it was possible to optimize the software stack, the networking and everything, and deploy our models running on AMD infrastructure. So, as Lisa mentioned, we just got hands on the Helios racks three months ago, and the collaboration has gone to the next level. Our engineers are working side by side with AMD engineers to optimize the software stack and run GPT class workloads on Helios already. Phil will probably be on stage later talking about how productive and fast that collaboration is. So we are really excited about the capabilities Helios is already showing us, and we expect that we'll be deploying Helios at massive scale starting towards the end of this year and then accelerating throughout 2027. Like I said, I know, more faster. We need it earlier.

Yeah, I think one of our key bets, and I think for a lot of us in AI, is about recursion, right? AI recursively improving the systems it needs to run on and eventually, obviously, AI doing the research itself, right? Today, expert engineers spend an enormous amount of time optimizing kernels, figuring out how to translate new models to run them efficiently on different hardware, dealing with all the usual things you think about, compilers, communications, libraries, system configurations. What we've been super excited and encouraged by the work we are doing with you is now AI showing the potential and demonstrating it in production to automate a significant portion of the work when anyone programs to an AMD GPU. So it dramatically has shortened the path from a new model coming up to an efficient production deployment and something that we can much more easily tune to changing workload requirements, right? And that's a big deal. So one of the other things that's super exciting for us is because AMD is built around an open software ecosystem, all these things that we are now inventing internally for our own use, we believe can, we can also bring it to the whole world and allow the whole world to use it for all of the AI models out there, not just our models.

If you haven't heard already, I need more compute more quickly. At least he's consistent. I think the thing that the whole industry is realizing is it's a systems problem at a data center scale. So I think we obviously very quickly internalized that AI was a rack level problem, but now it is very clear that it's not just a rack level problem, it's a rack in a data center. So the whole data center is a system. And we have to think about how we design the future based on what we know about our workloads, what's coming down the line, but co-design it with you from CPUs to GPUs to memory, networking, storage, power distribution in a data center, and the cooling systems that go with it. These are all intertwined and they all have to be co-designed. And one of the really fun parts about what we do with you and your team is how easy and productive it is for us to work with you and give you insight into how we expect the world to change in the future from what our workload needs are, what our models are, and how quickly your team responds in turning those insights into better chips, better systems, better software. So that's what worked really well with MI400 and left shifted a lot of the engineering work that we needed to do to bring this up online quickly. And that's why we are very confident we can productionize those systems very quickly. And so really looking forward to accelerating that with MI500 and beyond and increasingly using AI to actually do that work for us rather than being limited by humans.

Thank you so much.

It is so great to have you here. Thank you for being here. Santosh is a true friend. I have to say it's been a tremendous opportunity over the last few years. My story about Santosh is the first time I sat in his office. He said to me, Lisa, we're going to deploy a lot of CPUs. Please make sure they work. And I said, I will, I will. And you lift it up. I try. I try. But look, Santosh, Meta operates, you operate some of the most advanced infrastructure in the world. What's happening in the data center world? What's happening in your world? Tell us a little bit about the architecture and what you're working on.

Sure. First of all, thank you. It's awesome. This is like a hardware geek's paradise, right? You come in, you're talking about transistors, you're talking about hardware, talking about cooling. This is sort of the gem. I like it. And I think there's an audience that's receptive to it. I usually talk to audiences. They have usually either not been to a data center or not really seen a chip ever. So this is refreshing, I have to say. The thing I'll say about... Listen, we are seeing demand go through the roof. When you look at inference, training, recommendation system, that's how a newsfeed works. Content creation. The demand for that is just exponential right now. And when I think about sort of the overall... Mark has this vision of delivering personal super intelligence to billions of people. So if you go to sort of Facebook or Instagram or WhatsApp or whatever surface you go to, the idea is that we'll meet you there and we'll deliver super personalized intelligence right wherever you are. And that's why we established this Meta compute initiative and because it's a top level thing, Mark himself oversees. Now what it means is that we are now moving away from an area where we used to take servers, optimize it. This is what the conversation we were having many years ago, that, hey, I'm deploying a CPU, I really need to maximize performance out of it. It's different now. You talked about how we should be thinking about the whole system end to end. We are at a point where we have to look about the whole data center and think about it as one integrated system. Servers, hardware, networking, cooling, power, all of that is not optional. All of that has to work together. And this is where I think there's a huge opportunity for collaboration with partners like AMD because you have to now co-design, co-create systems, not just deploy something that comes off the shelf. The other thing is, by the way, flexibility. The thing I really like about what you said is that this is such a big opportunity in the industry right now. All of us need to lean in and work on this together. So it needs to be open. It needs to be heterogeneous. And I think that's not one company, one partner that works for any one of us. We need to sort of work on this together. And that's why I think you're one of our most important strategic partners.

Sure. It's been many years. And the story that Lisa was saying was many years ago. I think we went from Milan to Bergamo to Turin, now to Venice. So it's at least the fourth generation. So a long-time partnership, obviously. I actually think the world is changing in the sense that it used... The world is changing. Every year is a new world these days. It's like every month. Months, exactly. So what ends up happening now is that it's not just a GPU game anymore. It's CPUs and GPUs. If anything, I think CPUs are becoming at least as important, if not more. You're looking at a world where sort of the workload is changing because while you have your workhorses and GPUs, at the end of the day, there's agentic workloads. You have to run your tools. You have to run your systems. And all the code that's been generated still needs to run somewhere, right? So I'm super excited to work on Venice. I think there's a lot to come there. And you have to think again. This is a theme that I'm assuming will go throughout the presentation that you have to start thinking about CPUs and GPUs as conjoined things. You hand off workloads. Depending on the workload, you employ the right hardware.

Again, multi-year collaboration. I think we started with MI300. We did a bunch. It had really nice memory capacity. I remember talking to you about that, right? And then there's a bunch of pipelining we did. It was important to just have deployed that at production scale, sort of get hands-on experience on both sides. 350 is the first time we deployed this on our ranking and recommendation systems. So that's now getting into sort of some good numbers. And 450 is, I think, I'm super excited about it. We just got some of the racks. And we are going to deploy it across the board because it gives us the opportunity to go and sort of really collaborate. I think the big difference between the 300, 350, and 450 is that we have had engineers sitting in the same room talking to each other, co-designing, co-creating, like power cooling. Like I was saying, it's not just a single thing. And we are not shy in feedback. They're not shy, for sure. But you're very receptive. So this is true partnership, right? This is the point about co-designing and co-creating that, I think. And like I was saying, that's a huge sort of deployment coming in. The more we deploy, the earlier we co-design, the better we are.

Listen, all of you know about the bottlenecks. You know about the power, the data centers, the silicon. All of these, I think, are choke points that I think the industry is waiting on. But those are things I'm pretty confident we'll sort of work our way through. At the end of the day, I think the demand is immense. People are responding. But the thing that I really want to make sure all of us realize is that we should not think about systems in isolation. Like I was saying, CPUs and GPUs are one conjoined system. We should start thinking about it that way. We really need to co-design things early. See, data centers take years to build. One of my favorite stories is Mark comes to me and says, hey, I want a gigawatt worth of data centers. Well, you should have talked to me two years ago. It takes time. It's the same with silicon, right? It just takes time. So we need to be starting and sitting down in a room co-designing today for what we need to deploy in 27 and 28. That, I think, is when we truly unlock all sort of powers of the system. The systems are amazing. The specs are amazing, right? But think about how much more you can get out of it if you sit in a room and just co-design it today. And in 28, we'll be having much better graphs out there.

Andrew, it's great to have you here. It's been an exciting few months for you. I know that for those who don't know exactly what you've been working on, can you talk a little bit about Cerebras and what problem you've been trying to solve?

Sure. Great to be here and great to be sort of among people who love hardware. Yes, it's nice. Look, I'm thrilled to be here with you and to announce our cool new partnership. At Cerebras, we build the world's largest and fastest chip. It's a full wafer. And we package this into a system into racks and then racks into clusters. And we deliver them both on-premise and via the cloud. And you've had some of our customers up here already today. On the Frontier Labs, we serve customers like OpenAI and in the hyperscalers like AWS, in the small agentic and the coding space, leaders like Cognition. And they do this because we're blisteringly fast.

Sure, I think what's happened, and you described it previously, is that sort of AI has moved from being a novelty to being useful and then in some domains a necessity. And when something is a necessity, people want to use it and they want to use it quickly. And to serve this market, this segment of ultra low latency, we sought a partnership that could extend our capabilities and our footprint. And there was no better answer than AMD. We were already an AMD customer as we use AMD CPUs to surround our systems. And we were so excited at the opportunity to build a disaggregated solution that combined AMD CPUs, the cool new Helios rack and the cerebrus wafer scale engine.

Sure. So as Lisa described, what you have with Instinct and the Helios rack is that you have the leader in performance and memory capacity. And you marry that with our wafer scale engine, which is the leader in sort of in SRAM and in memory bandwidth. And that combination allows us to deliver a solution that is unmatched in the industry. Very, very exciting.

Later this year. It will be first in the cerebrus cloud and it will be later at a store near you.

And I think we're very, very happy to be able to do this together with you. So I think it's a huge step up in terms of what we can do for this ultra low latency segment.

I think until recently, customers sort of, they could have sort of high throughput or they could have extraordinary speed. And by bringing together the Helios rack with the cerebrus wafer scale engine, we give you five times the throughput while continuing to deliver this extraordinary speed. It's really something amazing.

Soon. Thank you.

Thank you so much.

Thank you, Lisa. Good morning, everyone. Good morning. It's great to be back to talk about software. Just a few years ago, programming AMD GPUs required deep expertise and significant engineering effort. We've come a long, long way in a remarkably short period of time. Through our open source community collaboration and sustained investment in ROCm, our software stack, AMD Instinct GPUs are powering some of the most important and consequential workloads on the planet. But the biggest change is still ahead of us. AI is transforming how software is getting built, and we are bringing that transformation to ROCm. And that's what I'm excited to share with you guys today. Our software teams have been moving fast with relentless focus on developers. We've invested at every level of the stack in build and test infrastructure so we ship faster. ROCm releases now go out every six weeks, not every four months. We've continued to expand the work we do with our AI ecosystem partners. And the result? More features, faster performance, and a significantly better out-of-the-box experience. Our strategy that has gotten us here has been remarkably consistent. It's built on two core principles. Partner deeply with the open source ecosystem and build with the right layers of abstraction to enable developer productivity. Open source gives us velocity and scale, and abstraction makes developers more productive. Our deep, deep commitment to open source has resonated with the community that has truly embraced AMD. From frameworks and compiler stacks to inference engines and model hubs, AMD is now becoming part of default enablement for the most important AI communities, such as Hugging Face, PyTorch, JAX, vLLM, and SGLang. This is why new models get Day Zero support and why the world's most important AI workloads run on AMD today. That is a big shift. Inspired by engineers seeking productivity, abstractions have evolved to enable work at the right level of detail. Hiding complexity when they want speed, and exposing control when they need performance. From low-level programming in assembly and C to block programming abstractions like Triton to Pythonic frameworks, we've invested in key abstractions. Aiter, our kernel library, and Atom, our serving engine, get you peak performance without writing the kernels yourself. FlyDSL is a brand new Pythonic domain-specific language that gives you low-level control with the performance of hand-tuned C++. And Mori, our communications library, helps deliver leadership performance on the latest models, like Minimax. But look, something very, very exciting is happening. Over the past year, I've seen something remarkable inside AMD. Our engineers are using AI models to generate GPU kernels, optimize code, debug issues, and improve performance. And in some cases, these AI-generated kernels are shockingly good. Better than what we expected. Sometimes better than the most manually-tuned versions. The first time you see this, you're a little bit skeptical. You run more tests, you try to break it, you look for what went wrong, and then you realize this is real. What has happened in general software development is coming to significantly transform GPU programming. It's going to reduce the time it takes to bring up workloads, make optimization automated, and it will remove any remaining barriers to broad adoption. We want to put that capability in the hands of every developer.

So today, I am excited to introduce ROCm.ai. It's an agentic AI platform that brings the capabilities of AI-assisted GPU programming to developers. It starts with the simple idea of giving developers access to the power of an AMD ecosystem through the AI coding agents they already use. Whether you're using Cursor, Claude, Codex, rocm.ai helps those agents understand AMD platforms, understand ROCm, and helps you build and optimize your workloads. In other words, we are making those popular coding agents into ROCm super users. You should be able to describe the workload you want to run, the performance target you want to hit, and then let the agents help you get there. For years, we have been building the open software foundation. Now, we are adding an AI-assisted layer that helps developers use that foundation faster and more effectively. So let me show you what is inside ROCm.ai. It's built on the foundational capabilities of ROCm, our core software stack, runtime, libraries, compilers, tools, framework integration, all of the core software that makes the stack work. On top of that, we built a layer of AI-assisted optimization called Hyperloom. Using Hyperloom, the system can analyze the workload, tune configurations, select and tune kernels, adjust parallelism strategies, and iterate towards performance goals. To give you an example of the power of Hyperloom, I was talking to the lead developer last week. He told me they just pushed through a suite of 14,000 models through Hyperloom, optimizing them and creating valuable insights. This would have been impossible to imagine, even with a large team of engineers before. That's the power of Hyperloom. And finally, we provide an AI-native interface for our developers, AI skills that allow coding agents to understand ROCm natively. I'm incredibly excited by what we have built with rocm.ai. It's going to profoundly transform the way developers interact with our platforms. So let me make this concrete for you with an example. Imagine you are a developer who wants to optimize Minimax M3 with vLLM on MI355s. Today, there are many things you need to get right. You need the right vLLM optimizations, the right model recipes, the right environment variables. You may need to write new kernels or optimize kernels, and then you still need to tune all of this. With rocm.ai, this interaction becomes much simpler. So let's see it. You can say, optimize Minimax M3 with Hyperloom. Enter. And behind the scenes, the agent does what an expert would do. Pulls a known good recipe, runs and profiles the workload. It creates and tests a bunch of kernel and runtime configurations, and it keeps iterating towards target. You can actually watch on screen here rocm.ai write a GPU kernel. It sees an opportunity to write a more optimized MOE group gem. And the result? rocm.ai delivers a 38% more tokens per second improvement on this specific example. This is the experience we want for developers. Simple to start. That's pretty cool, huh?

Simple, simple to start and powerful to optimize. We've continued our relentless focus and performance. With every release, we've delivered significant performance gains. On leading models like DeepSeek, rocm.ai delivers 3.3 times speed up over ROCm 7. This has been made possible by innovations at every level of the stack. Techniques such as the use of block-scaled fused MOE kernels, KV cache quantization, export parallelism. But look, the bigger story is how AI agents actually profiled the workload, proposed new kernels, tested configurations, and actually validated all of the results. That exact same focus is also on delivering performance gains for training. On the same hardware, rocm.ai improves training performance by an average of 2.4 times. Through optimized kernels such as fused flash attention, more efficient checkpointing, and advanced parallelism strategies. Our engineers have done amazing work to unlock the capabilities of MI455 through rocm.ai. So I'm delighted to share some incredible results on hardware. On running real workloads, we are able to see the system deliver 20 terabytes of memory bandwidth and 20 petaflops of delivered compute. These are the highest demonstrated compute plus memory capabilities of any accelerator platform in the industry today. It's not theoretical, there's measured. Super exciting that we were able to share this on day one. I'm also thrilled to share that the leading AI ecosystem partners who have had their hands on MI455 have had a delightful experience. You can see here what they've had to say. From end-to-end PyTorch testing, to ensuring Hugging Face models are getting validated, to vLLM and SGLang running well on MI455. We are incredibly encouraged and excited by what the community is about to unlock with MI455. This matters because day zero readiness is one of the most important objectives we strive for, and that's what rocm.ai enables. So now let's look at rocm.ai in action on Helios. This time we are going to use a Codex front end. Let's deploy DeepSeek V4 Pro, a 1.6 trillion parameter frontier model on Helios. Now watch what happens. rocm.ai pulls the right recipes, it configures the runtime, it implements the model graph onto hardware, and finally serves up the model. There you go. The model is actually up and running. And let's see the model write a simple poem now about advancing AI. Let's hit enter. Isn't that a pretty cool poem? This is the experience we want to give our users. The developers should not have to manually reason through every layer of model architecture, kernel selection, COM strategy, and rack level deployment. AI is transforming this experience, and that is a very, very big shift for GPU hardware and software.

Now, there are few organizations shaping the frontier of AI as much as OpenAI. You heard Lisa and Sachin talk about our partnership. It's been truly wonderful collaborating closely with the OpenAI technical team to make rapid progress with ROCm. To talk about how they are advancing the frontier of software development and our collaboration, I'm delighted to welcome to stage Philippe Tillet from OpenAI.

Thank you for joining us here, Philippe. So just so you all know, Philippe is the creator of Triton, one of the most important software innovations in modern AI infrastructure. It's a huge privilege having you here.

Thank you. For sure. Well, first of all, thanks so much for having me. I was here two years ago. It's my great pleasure to be back now. A way of looking at things is there's no one way to fit all applications, and we really try to meet users where they are. So for that reason, we've developed multiple solutions. So for workflows where speed of iterations is more important, we've developed Triton, as you mentioned. So this is typically what researchers will use. But more and more, we've been in a case where we've had to hyper-optimize performance, and Triton didn't give us the level of control that we wanted. So we've developed Glion for that, which is a lower-level language. And this way, we've been really able to optimize our kernels for AMD, among other things. Our agents have become extremely good at both of them, and we've worked with AMD on both of them as well. More and more, what we're seeing is agents slowly taking over. They're getting just very good, as you just mentioned, at writing this kind of code.

Amazing. So look, I think the work you have done with Triton and now Glion, it's been really, really moving the ball forward for the entire industry. Now, we began our work together with 300 and extended at 350, but now we're collaborating at a different scale with 450 and Helios. So I would love to hear your thoughts on how that collaboration has been going, and how has that experience been?

Yeah, super well. It feels like ages. I think we started years ago on hardware co-design. You know, when MI450 was in the very early prototyping stages, I think we were able to work really well together, and it's so nice to see the outcome of that and the chip. And more recently, we've been working very, very deeply on software enablement for MI450, both for our models, but also for the broader ecosystem. So a lot of this development has been done open source. And yeah, we're collaborating on the full stack now, down to the very low-level LLVM code generation, so that all the nice hardware advances that came with MI450 can be properly leveraged in end-to-end applications.

Yeah, and you got your Helios racks, so tell us a little bit about that.

Yeah, it worked. We got the chip, and very quickly, a single engineer in a few days was able to confirm that everything worked. Obviously, we have to optimize performance more. But we're very confident we can get in a very, very good place.

Awesome. That's awesome to hear. So look, one of the more amazing things that we've done recently is our teams have collaborated even more closely in terms of using AI to program AMD GPUs. So we'd love to hear your thoughts and anything you can share with the audience.

Yeah, no, I mean, everyone in this room knows how much agents are taking over our jobs, in a sense, as kernel engineers. And it's been amazing just to see how much better they've got over just the past six months. And just now, they're extremely good at generating high-quality GPU kernels in a way that were not before. And it's been very great collaborating with AMD on just making sure that agents not only are capable enough, but also have all the right context they need to be able to make good decisions and good optimizations.

It's great to hear. I think the quality of code that's getting produced by AI-assisted kernel generation has been truly remarkable through our collaboration. Very excited. Now, as you look ahead, what crystal ball do you have for us for the future of AI software ecosystem and collaborations like ours?

Yeah, so there's two things that really come to mind. One, as we keep talking, is just how good agents are getting. And the other one is, I think, how much the open approach to software of AMD is enabling, not only for OpenAI, but I think for the industry as a whole. I talked a little bit earlier how we've been collaborating on LLVM co-generation. LLVM is an open source project. The entire compiler stack from AMD is open source. And that has allowed us to make our agents really good at very, very low-level co-generation, down to scheduling instructions in assembly. And this has led to very, very significant performance gain that I don't think we'd have been able to achieve in a fully closed source stack.

Yeah, that's one of the real sort of big value propositions that we have to offer, because everything that we do is in the open, and the fact that AI can actually pick that up is a big one. Thank you, Philippe.

Yeah, my pleasure.

What you just heard is extremely important. When we combine better abstractions with AI-assisted development and closed hardware software co-design, we can create some incredibly powerful capabilities, and that's exactly aligned with our strategy. Now, everything I have shown you so far has been about AI, but ROCm is also a great stack for developing high-performance and scientific computing applications. From frontier models to climate simulation, we use the same ROCm, the same library, same tools, same AI-assisted development, one open stack for every single workload. And in fact, when you look at our roadmap, we have retained that very strong focus on HPC and scientific computing. Just like MI455 leads in AI compute with FP4 and memory performance, MI430X leads HPC compute with FP64 and memory performance, two platforms purpose-built for two very different classes of workload. The MI430X is built by leveraging our modular chiplet architecture to deliver a purpose-built variant with native FP64 hardware, giving customers leadership performance across AI and HPC in a single platform. And this is hardware double precision, not emulation. For the scientific community, that distinction is everything. This product delivers 288 teraflops of FP64 compute, nearly nine times better than competition. And the same leadership, memory capacity, and bandwidth of MI455. The MI430X ships in the first half of 2027. This is why leaders in sovereign AI love this product. The first sovereign exascale AI factories in both US and Europe are being built on the MI430X. Discovery at Oak Ridge National Labs and Alice Recoque with Cines and Genci in Europe. So look, as we look ahead, let me leave you with this thought. Every so often, our industry goes through inflection points. We've all felt it when neural networks first started recognizing cats and dogs, when transformers changed what was possible with AI. I believe we are experiencing another one right now, a real inflection in the ability of AI to program complex hardware systems. Building on the surface area that's been exposed from open source software and leveraging the power of abstracted interfaces, AI is making it dramatically easier to program AI systems. We cannot wait what you will build with it. Thank you. Now, AI is getting pervasively infused into all forms of computing. Enterprises are experiencing the same shift with their own unique requirements. So to tell us more about that transformation, please welcome to stage Senior Vice President and General Manager of Compute and Enterprise AI, Dan McNamara.

Thank you, Vamsi, and good morning, everyone. It is a great pleasure for me to bring to you the third pillar of our strategy, which is powering AI everywhere. And the vision behind this has remained the same for several years for us, to deliver the right compute engine to the right workload, and then continuously drive optimization points across our entire portfolio, from CPUs, GPUs, networking, and FPGAs. So building on what we've already shared, I'll cover how our strategy comes together across our enterprise portfolio and how the foundation we built with the CPU franchise has truly positioned us for the next era of AI across multiple deployment models. So Lisa talked about the frontier model and hyperscale landscape and just the rapid and massive advances we've seen over the last 6 to 12 months. And those advances are driving the adoption of AI well beyond the cloud. And as AI continues to grow, it will demand compute beyond the massive purpose-built data centers that we all know and love, extending into enterprise, personal, and physical AI. And each of these brings a new or unique set of requirements. Enterprises are usually constrained by power and cooling. Personal AI usually must operate within a fairly limited compute and memory footprint. And physical AI must perform reliably in real-world demanding environments. And AMD is the only company delivering a complete portfolio that spans all of these development models. So I just mentioned that our journey began in the data center with our CPU franchise, and I want to touch on it a bit. With each generation of EPYC, our strategy has been to listen to our customers, address their evolving needs, and deliver the highest performance, lowest total cost of ownership, and the fastest time to value. As Lisa mentioned earlier, we've introduced many industry-firsts across our generations that our enterprise customer has actually asked for. And that customer focus has brought us to where we are today, with EPYC as the leader in enterprise computing. And over the last three years, there's been very, very strong adoption across the enterprise, with the world's largest companies moving more and more workloads to AMD. And it's reflected in a number of places. First, public cloud, where our VM consumption has grown at 76% CAGR over the last three years. And on-prem deployments with our OEM and ODM partners is growing quite aggressively across key industries. But just as important, this position allows us to understand truly the diversity of the workloads that our enterprise customers run every day. And each workload faces new demands on the CPU. Some require maximum thread count, some depend on per-core performance, while others depend on memory bandwidth, cache efficiency, or I/O performance. The really important point is that there is no single SKU that solves every enterprise application. And that's why our EPYC portfolio with Venice spans 8 to 256 cores, with a range of power, frequencies, and I/O capabilities, giving customers the flexibility to choose the right solution for their workload, and most importantly, at the most optimized cost. So, Lisa mentioned this earlier, and now we all know, we covered Venice, and she showed how we have strong leadership across AI host nodes and the agentic workflows. But I wanted to show you how Venice actually leads across the general purpose workloads of the enterprise and these workloads that are running our customers' line every day. As you can see, we have a minimum of 2.5x the performance across our competition, across these very, very important enterprise workloads. So now, as we all know, agentic AI is really the next major transition for the enterprise. And it truly is a force multiplier for enterprises, and adoption is accelerating. In fact, the latest data here shows that nearly every enterprise next year will have some form of agentic deployments. And as enterprises move from pilots to production deployments, we've talked to CIOs all the time, and they are looking at three major things. First, predictable infrastructure cost and token cost. Secondly, making sure the data is secure. And then lastly, and sometimes this is overlooked, right, integrating AI into their existing infrastructure. It's not all greenfield out there for the enterprise customer. So to solve these problems, we truly believe that this is going to be a distributed deployment model that leverages frontier models, cloud services, on-prem infrastructure, and of course, AI clients. And as AI has moved from chatbots responding to prompts to agents doing real work, writing code, summarizing documents, and running complex flows, the requirements have evolved also. And those are exactly the requirements we've been designing for for multiple years. So while AI will be deployed across all these environments, the on-prem deployments in our enterprise customers have fairly unique constraints. First and foremost, power/cooling. Really big challenge in space. And of course, costs remain top of the list for CIOs that are trying to drive things forward in the AI world. And then lastly, the full solutions they need to be easy to deploy. That's been common for many, many years for the enterprise, but that's something we're very, very focused on. So to address these constraints, thank you, Laura, I'm very happy to announce the launch of the Instinct MI350P. So today, every enterprise wants AI in the data center, but the challenge is how do you add those capabilities without starting over? That's exactly what the 350P was designed to solve. It's an air-cooled GPU designed to fit within the power and cooling of enterprise servers today without a facilities upgrade. Making it easy to bring LLM scale inference into today's enterprise data center. And at the same time, there's no compromise in capability. A single MI350P can support up to 260 billion parameters, allowing customers to run the majority of enterprise AI workloads on a single GPU. And the economics are actually even better and more compelling. The 350P delivers more than four times the tokens per second per dollar than the competition. It's pretty cool. And it actually turns the customer's existing data center into an AI data center. Now, we all know that benchmarks are one part of the equation, right? The other part is really evaluating, does the advantage hold up across the actual workload you run and how your business runs? So we tested a set of workloads, enterprise use cases, using production models. And the 350P delivers much higher productivity than the competition, delivering up to two to five times faster tokens per second than the competition. So if you think about that, that's more tokens per second, more work completed, more users supported, all at a lower cost for the business. So another area that I wanted to talk about is, our earliest customer is our own AMD IT team. And they're a tough group to work with, even for us. But they put us through our paces. And really, this has been our model from day one is, we want to put our technology into our own data center first. So like many companies, many enterprises, we're looking at how to deliver AI service to our employee base, but also manage costs and protect our data, manage and control the data. So we focused on two use cases that we wanted to try out. And first was autonomous threat detection. And that is probably one of the faster-growing agentic applications that we're seeing today. And the second was a personalized AI assistant running on OpenCloth. So both of these run route requests through the router, and the gateway sort of tests the complexity, the sensitivity of the information, and the service level requirements. So some requests are sent to the Frontier model, while others are routed to OpenWeights models running on EPYC and an MI350P. So with intelligent routing, we reduced our token costs by 43%, while delivering up to 3x faster response times for the workloads running locally. So we believe this is how enterprise AI will be deployed. But we also know that, from our history here with EPYC, that enabling enterprise takes a whole lot more than great silicon. It requires a complete ecosystem of software and solutions. And over the last several years, we've truly grown significantly and expanded our enterprise AI ecosystem. Today, we support numerous AI ISVs, provide zero-day support for all of the industry's leading open models, and deliver robust frameworks that help customers bring AI into production as fast as possible. And it's all built on trusted platform software and OEM partnerships that enterprises rely on every single day. Now, everything I just shared comes down to one goal for us, and that's helping customers deploy AI where it creates the most value for them, whether it's in the cloud, on-premise, or at the edge. So to bring this to life, I'd like to welcome Jeremy Legg, Chief Technology Officer at AT&T.

Good to see you. Thanks for having me. Yeah, this is great. It's kind of a homecoming. I grew up in the East Bay, so it's good to be back.

Yeah, fantastic. We really appreciate you joining us today. So look, I think everyone knows AT&T as the largest communications company, one of the largest in the world, and I'm sure half of them out there probably have AT&T network right now. Hopefully more than half. But AI is touching way more than the network, and you and your team are really doing a fair amount of work driving it. So can you share with us sort of the opportunities you approach, you're working on, and your experience to date?

Sure, happy to. At AT&T, we're now burning about a trillion-plus tokens per month, and that number has gone up pretty dramatically. It's moving up double digits, and so this is now becoming very pervasive inside of the enterprise. We've got over 100 Gen A models in production across our enterprise as well, and those are stretching from things that I think a lot of folks are doing in customer care, things that we're doing in fraud, but also things that you wouldn't necessarily think about, like where's the best place to place a cell tower or a RAN that is the most effective use of it inside of a network. So all of these things are happening across our enterprise, but they're happening at a scale that is not necessarily normal. We get over 300,000 calls a day. The transcription of those calls is an enormous workload, and then driving the insights out of those calls that we then bring back to the business is another set of insights. So it's really an interesting thing, and now we're beginning to actually rebuild entire workflows inside of the company beyond just the point use of a use case. So as we think about HR, as we think about finance, and we think about different kinds of things that each of those organizations do, cash forecasting, things that we do on onboarding employees as examples, we're now agentifying those entire workloads.

Yeah, so we work closely with your team, and we've had many conversations. It's truly amazing the number of actual opportunities you're addressing with AI, so a super broad range of use. So can you just explain sort of what are the key learnings in moving these to AI at enterprise scale, and then maybe a bit on what we've done together?

Sure. I mean, I'll start at a place that I don't think everybody starts from here, which is the human. You need world-class teams in order to do this, and you need people that think in workflows. There's an enormous amount of what I call racing from stoplight to stoplight. Folks are going from 0 to 100, and then they hit the next stage of the workflow, and then there's a red light, and they haven't worked their way all the way through that. So I would start with people and the quality of the people that you really have across your enterprise in order to do these things. The second point is we are big believers in data sovereignty, and data sovereignty for us means not being tied to a specific chipset, not being tied to a specific model or a specific set of development tools. You need, as an enterprise, to manage your data. Your data is your fuel in terms of how you implement AI across that broader enterprise. What we've done with AMD, and AMD's been such a great partner in this, is leveraging open source models, leveraging different sets of chipsets, building our own models, training other models. We're one of the few companies in the world that's actually post-trained a model on AMD and then been able to drive equivalency in terms of performance and accuracy with other more expensive chipsets in the marketplace. So as we have done that, we've been able to drive down token costs, essentially through money-balling across all of these different models and leveraging that human capital in a way that's made a big difference to our company. And so increasingly, even though token consumption is going up, we're able to manage that underlying token cost at enterprise scale, which is not a small thing.

Yeah, it's a bunch of great work. Another key area that's sort of groundbreaking, which is exciting also, is you're the first telecom company to actually train a model on AMD. And also, you're driving fully open source models in telecom. So tell us about that experience and why you felt like that was needed.

We felt like the industry was moving down a path where folks were only going to use closed source models to solve solutions. And while closed source models are an important ingredient in that, we didn't want to lose sight of the fact that open source models were going to be a very significant part of the solution. So we announced what we call OTEL 1.0. That's the open telco AI model a bit over a year ago or so now. And we had over 18 million downloads of that model since then. We're excited to announce today the launch of OTEL 2.0, which is a more advanced set of models that we have trained on AMD. And we're now making that available via open source across the broader industry. And it, I think, is going to give folks an opportunity to see that not only do you need to think about leveraging models, but you also need to think about training those models and how they work across your enterprise.

So it's just great. We certainly appreciate the partnership. And, you know, we really feel like this is germane to the conversation. It's exactly what we've been talking about today. And I just want to thank you again for joining us and the partnership. Thank you.

You're a big part of it. Thank you.

Okay, so let me close with one last point. We've established ourselves as a leader in enterprise computing by listening to our customers and delivering the right solutions for their workloads. Today, we're extending that leadership to help customers accelerate their AI journey. This is our vision for powering AI everywhere. And to expand a little bit beyond the data center into physical and client AI, it's my pleasure to introduce Mr. Jack Huynh, who leads our computing and graphics group. Thank you.

Thank you, Dan. It's great to see so many friends, partners, and developers here in San Francisco. Today, we have seen how AMD is building the compute foundation for AI in a data center. But the full AI transformation requires two more ingredients. Intelligence that becomes personal, and intelligence that can understand and act in a physical world. Those are the two frontiers I want to explore with you here today. Let's begin with personal AI. Enterprise AI is already reshaping the data center, but agentic AI will extend far beyond it. Agents are the next great productivity multiplier. The jump from software that responds to software that completes work. A personal agent can double what one person can do. A team of agents can multiply that by 10. And as agents gain more capabilities, the opportunity can grow exponentially. But that promise has a compute consequence. Unlike a single AI query, as agent reasons, calls tools, and coordinates to other agents, and run nonstop, agents become more capable and pervasive. So will token consumption, meaning that demand will require every layer of the computing ecosystem to scale together. Data centers remain the foundation for frontier AI in the most demanding workloads. But edge-inclined systems can extend that capacity, putting more intelligence closer to where data is created and where the work gets done. The PC is already one of the most powerful compute engines ever placed in the hands of an individual. Across billions of devices, enormous computing capability is already deployed every single day, yet its potential remains largely untapped. Putting that capacity to work changed the economics of computing. More workloads can run on systems that are already in place, reducing overall infrastructure costs and allowing data centers to focus on the largest, most demanding task. And local computing brings inherent advantages. Your data stays with you, your applications remain responsive, and your system keeps working, even when your network does not. And thanks to a new generation of smaller, more capable AI models, we're beginning to unlock that potential. And the unlock is happening faster than anyone expected. Last August, GPT OSS needed 120 billion parameters to score 80 on GPQA. Just seven months later, Qwen 3.5 scored even higher, with only 9 billion. Better performance, with 13 times fewer parameters. This is not an incremental improvement. It's a dramatic shift in the compute required to deliver advanced intelligence. And this is a trend, not an outlier. Smaller, open models are closing the frontier gap at extraordinary speed. Qwen 3.6, 27 billion parameters now outperforms leading frontier models from the previous generation on graduate-level reasoning, at a fraction of the size and cost. That changes what is possible. Workloads once reserved for the cloud can now run on powerful compute engines designed for personal AI compute. Let's map these model performance breakthroughs to any platforms. Ryzen AI 400 supports models up to 24 billion parameters, more than enough for Qwen 3.5, 9 billion, with substantial headroom. Ryzen AI Max extends that capacity to models as large as 200 billion parameters, running natively on a personal system. This has not reduced AI, compressed to fit on a PC. It is a new class of personal computing built to run powerful models where the work actually happens. And this is exactly what led us to build Ryzen AI Halo. We wanted to give developers a platform powerful enough to run these models locally, yet simple enough to use every day. With 120 gigabytes of unified memory and support for models up to 200 billion parameters, developers can build, test, and iterate locally directly on their desk. Ryzen AI Halo embodies the future of personal AI for everyone, a future that shortens the distance between an idea and a working model. For developers, that means a dramatically more efficient and optimized path from experimentation to deployment. And as Vamsi showed earlier, rocm.ai is making hardware programming radically more accessible. Developers can understand, modify, and optimize code for ROCm, even without deep ROCm expertise. And better yet, the same open software foundation extends across all of AMD's hardware. Developers can use the frameworks and tools they already know, start locally on Ryzen AI Halo, and scale across the full AMD compute platform without rebuilding their work. And we're not stopping there. Today, we're expanding our partnership with Hugging Face to bring the open AI ecosystem directly on Ryzen AI Halo. Powerful compute only creates value when developers can quickly access the right models, optimize them for the platform, and turn them into real applications. Together, AMD and Hugging Face will deliver Halo optimized performance for open models and agentic workflows. How cool is that? And along with co-engineered libraries and toolkits designed to help developers spend less time configuring infrastructure and more time building. And later this year, every Ryzen AI Halo box will include a full year of Hugging Face Pro, putting the power of partnership in developers' hands from day one. Freedom and more power to the user. This is how we accelerate innovation in the AI era. And we're already pushing that vision further with Gorgon Halo. And seeing is believing, look at how small and beautiful this box is. We've increased unified memory from 128 to 192 gigabytes. The largest unified memory pool in this class. And increased model support from 200 billion to 300 billion parameters. Thank you, Laura. And this is no longer limited to a single platform form factor. Together, with the world's leading OEMs, AMD now powers the broadest portfolio of personal AI systems. From laptops and workstations to mini PCs and developer platforms. That means developers, enterprises, and creators can choose the right system for the way they want to build and work. Personal AI is not a concept. It is a category and is available right now on AMD. We now have all the pieces. Models are efficient enough. Hardware is powerful enough. Software stack is open and ready. And open harnesses like Lemonade Server can route workloads to the right model and right compute. The technology is here. The next challenge is taking it from one developer's desk to thousands of users across an enterprise. Cisco sees and believes in that future the same way we do. To take us deeper into how we turn that vision into enterprise scale reality, please join me in welcoming to the stage Jeetu Patel, President and Chief Product Officer at Cisco.

Jeetu, how are you doing? How are you? You look great. Thank you for joining me here today. Congratulations. Thank you, thank you.

Now Jeetu, you and I talk how we're at an inflection point. And for the past few years, industry has spent time just building intelligence. The next phase is how we deploy this technology at scale enterprises. You spent so much of your time talking to CIOs, technology leaders. What are they telling you? What's changing?

Well, firstly, it's a really exciting time to be alive in tech. And if you just take a step back right now and see what's happening, there's actually a fundamental shift that's happening in the patterns of inferencing. So if you think about the patterns of inferencing that existed during the chatbot era that was human-led, it was very, very spiky. You ask a question, you get an answer. What you're starting to see with agents is they're working 7x24. They're very consumptive on bandwidth and on infrastructure. So you're starting to see a much more persistent pattern of kind of demand signal for infrastructure. You know, we usually joke around internally, humans click, but agents swarm. And so as you start to see this, what you're going to have is a tremendous amount of and there's two big concerns that are also there, which is one is every customer is concerned about security and every customer is concerned about cost. So what you're starting to see happen is inferencing is not just going to be limited to the data center. Inferencing is going to be distributed everywhere and there's a whole new class of computing that's emerging with this desk-side computing that you just announced where there's going to be inferencing that you're going to have a laptop or a desktop where humans work, and they might have a desk-side computer where agents are kind of going out and running their jobs on behalf of humans. So that's what we're seeing, essentially.

We're fully aligned Jeetu and that's exactly the shift we're seeing. If AI is moving closer to employees, compute has to move closer as well. The PC's knowledge as a productivity device becomes an intelligence node in the enterprise but deploying intelligence everywhere creates a new trend for CIOs that you and I talk about so much. How do you operate thousands of these systems with the security, governance and control enterprises require? Jeetu, how is AMD and Cisco solving this and what are we building together?

Yeah, so this is an exciting time because what you're starting to see is there's a few things that need to really be done. You can't just go out and deploy your desk-side computers and hope that everything works out in the enterprise. You need to make sure that it's governed, it's managed and so there's a few things that are really important. Number one, you have to have an appropriate amount of network bandwidth to satiate the needs of the agents that you are going to run locally. So this notion of network infrastructure that can get modernized to keep up with the needs that you're going to go out and generate is going to be very important. That's number one. Number two, like I said, every customer is really worried about token costs and so we need to make sure that we can actually contain the costs of an agent's consumption of tokens when it actually starts to go awry. Up and down when needed. Exactly. Number three then is how do we monitor agent behavior for safety and security so that if it does start to do things that we don't want it to do, we can in runtime provide enforcement guardrails. And then fourthly, it's just the overall safety and security that you need to have from an apparatus perspective to get this entire thing humming in the way that you want it to hum.

Yes, it's great how you and I think very alike. So I mean the opportunity is not just bringing every device as you and I talk. How do we enable enterprise to deploy, manage the skill with confidence? Why don't we take a look at what we've been building the past few weeks and months.

Yeah, it's exciting. So basically what we've done is there's a full stack that you can start to think about. So what you folks have done with Halo is you've got an isolated secure agent sandbox. You've got intelligent routing. You've got an MCP kind of core set of integrations. And then what we've done above that is we've made sure that you have the right level of security policy enforcement. We've also got the right level of observability so that it's an end-to-end resilient infrastructure stack. So it's observability of how is your apparatus and the infrastructure working. Is the agent behavior being done in the right way? Can you observe the agent behavior? And are the tokenomics within the guidelines that you want them to be? And then what we have is a single unified management plane, a control plane that can look at every single dimension that you have and be managed within one environment. And so that's kind of the full stack, and we are really, really excited about the partnership.

Us too, our teams love working with your team Jeetu. How is this all managed? How does the CAO deploy this at scale?

Yeah, so let's take a look at what this looks like, right? Because what we would have is this is Cisco Cloud Control, which is the overall management plane. So if you happen to have Halo devices on every desktop, which it might actually get to, what you want to make sure that you do is you are able to go out and manage your entire estate of not just your Halo fleet, but also what you have running on the data centers, as well as what you have running in the cloud. So you can provide full visibility for that entirety of the estate. And then once you've got that, what we also have is this notion of how do we go out and monitor tokenomics? And so is the agent behaving the way that it needs to behave, or is the agent actually going out and being overly consumptive? At which point, you should be able to isolate any individual Halo device so that you can make sure that you can quarantine it.

No, I love this. Then we can actually see the return on invested capital. It could pay for itself in three or six months even quicker.

Exactly. So what you see over here basically is you can tell how every single agent is performing and whether or not they're consuming way too much kind of token costs. And if they are, how do you actually contain the costs as you're going through it?

No, I love this Jeetu. Now, everyone else is wondering when can they buy this? When is this open for everyone?

Well, the good news is you can buy the Halo already today. Yes. And then what we're doing is we're making sure that we provide this entire management apparatus that's currently in early availability for a select set of customers. But we'll have it in general availability in the US in early fall. So we're excited to make sure that we can provide not just the compute, but all of the apparatus that's needed to go out and manage this so that you can have inference running in the cloud, you can have inference running in your data center privately, or you can have inference running on your desk side.

Jeetu, we're so grateful to be part of you and Cisco. We're so excited. Thank you so much. Congratulations. Take care, Jeetu.

Thank you.

What you just saw brings a vision of scaling AI seamlessly and securely for personal devices all the way to the cloud. But in the age of AI, intelligence will be pervasive and it will be everywhere. The next frontier is where AI leaves the screen, enters the world and acts. That frontier is physical AI, the ultimate application of agentic frameworks. In the digital world, an agent plans and completes a task. In the physical world, it must also sense motion, navigate uncertainty, and protect people. Here, intelligence does not just produce an answer, it produces an action. When answers become actions, latency becomes safety and reliability becomes trust. That is why the promise of physical AI is so incredibly powerful. It is not about replacing human potential. It is about amplifying it, giving surgeons greater precision, keeping people out of harm's way, making farms more productive, supply chains more resilient, and factories more efficient. The best technology does not make people smaller. It gives them much greater reach. For more than 20 years, AMD has helped power the core capabilities that robotics depend on, from sensing and functional safety to deterministic control and precision motion. That foundation is already trusted by millions of companies to find the future of robotics. And now, that foundation is coming alive in extraordinary ways. These are not machines following a script. They are beginning to perceive the world, reason through uncertainty, and act with purpose. The promise of physical AI, already in motion. The next era of robotics is not programmed. It is autonomous. While traditional robots execute instructions, autonomous robots are given an objective. And from there, they determine how to achieve it. And they do this all at once, and all in real time. The robot must orchestrate intelligence, motion, and safety continuously. And every part must respond at the speed of the world around it. That demands an entirely new kind of robotics brain. And to solve that, we rethought its architecture from the ground up. Today, I'm so excited to introduce the AMD Kria AI System On Module, powered by Ryzen AI Embedded X100. It brings CPU, GPU, NPU, and unified memory together in one compact, open standard architecture. This allows robots to process perception, AI reasoning, and do it all simultaneously and in real time. The architecture is different, but the results are decisive. In third party testing against NVIDIA Jetson Thor, Kria delivers 3.4 times better real-time results, 2.3 times more concurrent agents, and 1.6 times more CPU capacity. That means faster reactions, and greater headroom for the robot to take on more complex work. And all this without compromising control or safety. But to find the next era of robotics takes more than a powerful brain. Developers need a complete path from an idea to a machine operating in the real world. Today, we are introducing AMD's Kria AI Robotics Developer Platform. The industry's first turnkey open, fully integrated robotic system. Thank you, Laura, so much. At the center is the Kria AI System on module, paired with a robotics carrier card designed for sensing and connectivity. Built on ROCm and ROS2, this platform gives developers everything they need to move from concept to prototype in days. And the same module carries forward into production, with no migration or redesign needed at the finish line. You can build faster and move from imagination to deployment with absolute confidence. This is the breadth only AMD can bring to autonomous robotics from the first signal to the final motion. AMD Kria for the brain, Versal for the spine, Zynq for the joints, and Spartan for the sensors. Each layer purpose built for its role, working together in one intelligent system, connecting perception and high-level reasoning to real-time control. The future will not be defined by one model, one machine, or one company. It will be built in the open, across silicon, software, systems, and an ecosystem moving forward together. From the cloud to the PC and now into the physical world, AMD continues to build a compute foundation for intelligence everywhere. We'll laser focused on the future so our solution can augment and expand what people are capable of achieving. The next era of AI will not just unfold behind the screen. It will unfold in the world around us. And now, to bring it all together and take us to what comes next, please welcome back Lisa to the stage.

Hi, Lisa.