FULL EVENT: Nvidia’s Jensen Huang Unveils AI PC Revolution at COMPUTEX 2026 in Taipei — AI1B

[00:00:00] - In Wong. [00:00:10] - Welcome to GTC Taiwan. [00:00:16] So great to see all of you. [00:00:20] Very good to be home. [00:00:21] I brought my parents home. [00:00:23] Where are my parents? [00:00:24] Everybody give a round of applause to my mom and dad. [00:00:30] And a round of applause for our pre-game show, [00:00:39] Superstars, ladies and gentlemen. [00:00:46] Look how adorable they are. [00:00:50] The Superstars of Taiwan. [00:00:52] There are so many of you here today. [00:00:54] We are broadcasting this right now [00:00:57] to 70 other watch parties across Taiwan. [00:01:02] 70 different conferences are going at the same time. [00:01:06] Everybody is watching this keynote. [00:01:09] We have so much to tell you. [00:01:10] And I have so many partners to thank. [00:01:13] It is incredible how large our ecosystem in Taiwan has become. [00:01:19] Most of the time, when people think about ecosystem, [00:01:21] they think about our software stack. [00:01:24] they think about the developer ecosystem [00:01:26] above the computing systems that NVIDIA builds. [00:01:29] But NVIDIA's ecosystem spans all the way upstream [00:01:34] to all of our supply chain here in Taiwan, [00:01:37] where it all begins, [00:01:39] and downstream all the way to data centers [00:01:42] and eventually to end users. [00:01:45] Today, we're gonna talk about almost all of the ecosystem. [00:01:48] There are so many people to thank. [00:01:50] I love my ecosystem here. [00:01:53] I mean, there are so many companies here, [00:01:56] and some of my favorite ecosystem partners. [00:02:00] I mean, there are so many companies here in the world. [00:02:07] I mean, there are so many companies here in the world, [00:02:09] and there are so many companies here in the world. [00:02:10] I mean, there are so many companies here in the world. [00:02:12] I mean, there are so many companies here in the world. [00:02:13] I mean, there are so many companies here in the world. [00:02:14] I mean, there are so many companies here in the world. [00:02:15] I mean, there are so many companies here in the world. [00:02:16] I mean, there are so many companies here in the world. [00:02:18] I mean, there are so many companies here in the world. [00:02:19] I mean, there are so many companies here in the world. [00:02:20] I mean, there are so many companies here in the world. [00:02:22] I mean, there are so many companies here in the world. [00:02:23] I mean, there are so many companies here in the world. [00:02:24] So many, Taiwan's rich ecosystem, the richest ecosystem, [00:02:40] the world's best supply chain ecosystem. [00:02:43] Unbelievable. [00:02:44] Well, thank you all for being here. [00:02:46] And this year, our businesses together [00:02:50] are growing incredibly. [00:02:52] In fact, somebody told me last night [00:02:54] that the annual GDP of Taiwan is going [00:02:58] to grow almost 10%. [00:03:07] Unbelievable. [00:03:09] Well, we have a lot to talk about. [00:03:10] Let's get going. [00:03:12] Two years ago when I was here, I started [00:03:14] to talk to you about how AI has moved from generative AI [00:03:18] and the other waves of AIs that are coming. [00:03:21] The next wave of AI was agentic AI. [00:03:24] And today, we can say that agentic AI has arrived, [00:03:28] that useful AI has arrived. [00:03:31] Now, what does this mean? [00:03:32] This is GitHub. [00:03:34] This is, of course, one of the first applications [00:03:36] of agentic AI is software coding, [00:03:40] one of the most valuable professions. [00:03:43] Incredibly large ecosystem, 30 million, 40 million [00:03:47] software developers, probably another couple of hundred [00:03:52] who are students and enthusiasts and so on and so forth. [00:03:56] But say, 30, 40 million software developers in the world code [00:04:01] for a living. [00:04:02] And this represents most of them. [00:04:05] This is GitHub. [00:04:06] The pull request is when they download software, they modify it, [00:04:10] and commit is when they push it back up. [00:04:14] And so if you could look at this, in 2023, the number of commits was 300 million. [00:04:24] 2024, 400 million. [00:04:27] 2025, 500 million commits. [00:04:33] In the first few months, in the first few months of 2026, [00:04:39] it has nearly tripled. [00:04:41] Now, what does that mean? [00:04:44] 30 million software developers representing about $3 trillion worth of GDP, [00:04:52] producing three, that's what they're paid, $3 trillion worth of salaries per year, [00:04:58] which is generating economic growth for the rest of the industries. [00:05:03] Say $100 trillion of the world's industries is impacted, [00:05:08] is generated by $3 billion worth of salary. [00:05:13] That $3 trillion, excuse me, $3 trillion. [00:05:15] That $3 trillion worth of salary is now producing nearly three times as much output. [00:05:24] It's effectively a $9 trillion productivity from $3 trillion of salaries. [00:05:33] Does that make any sense? [00:05:34] The difference is absolutely extraordinary. [00:05:37] This is the potential. [00:05:38] This is the promise of AI. [00:05:40] The number of engineers, software engineers, is actually increasing. [00:05:43] People talk about AI reducing jobs, complete nonsense. [00:05:48] It's causing more software engineers to be hired, and the reason for that is very simple. [00:05:54] If you can hire a software engineer, and you could generate $9 trillion worth of productive [00:06:01] work, why wouldn't you want to hire more software engineers? [00:06:05] If that line was flat, then obviously people will hire fewer software engineers. [00:06:11] But because the output is so incredible, people want to hire more software engineers. [00:06:16] This is going to show up in our economy somehow soon. [00:06:19] And so the first thing is useful AI has arrived. [00:06:22] Now what does that mean from the industry's perspective? [00:06:25] From the industry's perspective, that means that tokens are now in extraordinary demand. [00:06:32] Because if you could do this, you're going to want to produce more of it. [00:06:35] And because tokens are now profitable units. [00:06:39] Tokens are now profitable units of revenues. [00:06:44] Because it is now profitable. [00:06:45] The AI companies want to build a lot more tokens, generate a lot more tokens, build more AI factories. [00:06:51] Which is the reason why compute demand here in Taiwan has skyrocketed. [00:06:57] It is precisely the reason why all of you are so busy and your businesses are doing so well. [00:07:03] In fact, that looks like some of your stock price. [00:07:15] The compute pattern has changed. [00:07:18] Everything has changed. [00:07:19] So the first idea is that useful AI has arrived. [00:07:24] AI is now a profit generator. [00:07:26] AI is now a GDP generator. [00:07:30] Behind it is a whole new kind of computing pattern. [00:07:34] Not just a large language model, but an agent. [00:07:36] So today, almost everything we're going to talk about is going to be based on this. [00:07:41] So let me take a quick moment and show you what I'm talking about. [00:07:45] Inside, this is an agent. [00:07:48] It's an agent application. [00:07:51] In the old days, this would be application. [00:07:55] This would be code. [00:07:57] And this would be operating system. [00:08:02] Application, code running inside an operating system. [00:08:06] Today, it is agent, which consists of a large language model or many, sitting inside a harness. [00:08:15] And that harness helps it, orchestrates it to do productive work. [00:08:21] This is the input. [00:08:23] When that input comes, it has to understand, observe, reason, act, use tools, use tools. [00:08:32] That tool could be a spreadsheet, web browser, a data processing engine, database engine, for [00:08:37] example. [00:08:39] This is orchestrated. [00:08:41] This harness orchestrated. [00:08:42] This harness orchestrate this routing of information. [00:08:47] Every single time it touches either processing the context, understanding what is happening, reasoning [00:08:54] about what to do, coming up with a plan that it acts on, that orchestration path is orchestrated [00:09:03] by some software. [00:09:05] And so this is fundamentally an agent. [00:09:09] It deals with short-term memory, called working memory, long-term memory, just like we do. [00:09:14] We have long-term memory. [00:09:16] And so the memory management system is incredibly important. [00:09:19] This entire system is called an agent. [00:09:23] The large language model is used to do the thinking. [00:09:28] And the harness connects everything together, just like an operating system, okay? [00:09:35] And so this is the new computing model. [00:09:37] And this is what an agent. [00:09:39] It could do incredible things. [00:09:41] This is the big breakthrough. [00:09:43] The simultaneous convergence of large language models that are now able to do a really good [00:09:49] job thinking, reasoning, planning, using tools, and the fact that we have now these harnesses [00:09:56] that manages memory, the orchestration, uses tools, we can now do amazing things. [00:10:02] Let me give you some example. [00:10:04] This is a prompt. [00:10:06] This is the prompt. [00:10:08] This is the code that is generated. [00:10:11] And this comes out. [00:10:14] This is the input. [00:10:17] This is the input. [00:10:19] And that's the output. [00:10:20] Do you guys -- what do you guys think? [00:10:23] It's pretty amazing, right? [00:10:29] We use cloud code here, but Codex does an incredible job as well. [00:10:32] Here's another example. [00:10:34] This is the input. [00:10:35] Create a GIF, NVIDIA green dots on black scatter, form Taiwan 101 building, morphed to GTC [00:10:45] Taipei 2026, morphed to NVIDIA i logo, then scatter and repeat, right? [00:10:51] So you saw that. [00:10:52] That was the prompt. [00:10:54] Here's the next one. [00:10:55] I lost my remote control battery clip. [00:10:57] It looks like this. [00:10:59] Create a CAD file. [00:11:01] It uses a tool, created a CAD file, ready for 3D printing to create a new one. [00:11:07] Make sense? [00:11:09] This is now the new computing pattern. [00:11:12] Whereas we used to launch an application, click and type. [00:11:18] We now replace that with explaining to the AI what we want, our intent, and the AI generates [00:11:26] the code or uses tools and produce the necessary output. [00:11:32] This is how computers are going to work in the future. [00:11:36] This is agentic AI. [00:11:38] For two years we've been building towards this, and now it has arrived. [00:11:42] Now one of the big breakthroughs, of course, is tool use. [00:11:46] A lot of people have said, you know, Jensen, AI is coming, agentic AI is coming, therefore [00:11:51] all of the software companies are going to go out of business. [00:11:54] I said it's exactly the opposite because there are going to be so many agents. [00:12:00] The world is no longer limited by the number of people, therefore those agents are going [00:12:07] to use more tools than ever. [00:12:10] This is actually an incredible time to be a software company. [00:12:14] But the software has to be presented to the agent in a way that the agent can use it. [00:12:20] This is a big breakthrough, and in fact what we have done, as you know, what NVIDIA's [00:12:26] treasure is, is all of our CUDA libraries. [00:12:29] I call them CUDAX libraries. [00:12:32] This is NVIDIA's treasure. [00:12:34] Today we're able to now present these CUDAX libraries to agents who can use it much more effectively [00:12:43] than even humans. [00:12:45] And so this is a wonderful time for CUDAX libraries. [00:12:48] Let's take a look. [00:12:52] 20 years ago, we built CUDA, a single architecture for accelerated computing. [00:12:58] We reinvented computing. [00:13:00] A thousand CUDAX libraries help developers make breakthroughs in every field of science and [00:13:06] engineering. [00:13:07] CUDAX libraries are tools for agents. [00:13:11] CUDAX libraries are tools for agents. [00:13:12] CUDAX libraries are tools for agents. [00:13:13] And so we're able to use the tools for agents. [00:13:14] We're able to use the tools for agents. [00:13:15] We're able to use the tools for agents. [00:13:16] We're able to use the tools for agents. [00:13:17] We're able to use the tools for agents. [00:13:18] We're able to use the tools for agents. [00:13:19] We're able to use the tools for agents. [00:13:20] We're able to use the tools for agents. [00:13:21] We're able to use the tools for agents. [00:13:22] We're able to use the tools for agents. [00:13:23] We're able to use the tools for agents. [00:13:24] We're able to use the tools for agents. [00:13:25] We're able to use the tools for agents. [00:13:26] We're able to use the tools for agents. [00:13:27] We're able to use the tools for agents. [00:13:28] We're able to use the tools for agents. [00:13:31] We're able to use the tools for agents. [00:13:33] We're able to use the tools for agents. [00:13:34] We're able to use the tools for agents. [00:13:35] We're able to use the tools for agents. [00:13:37] We're able to use the tools for agents. [00:13:38] We're able to use the tools for agents. [00:13:39] We're able to use the tools for agents. [00:13:40] We're able to use the tools for agents. [00:13:41] We're able to use the tools for agents. [00:13:42] We're able to use the tools for agents. [00:13:43] We're able to use the tools for agents. [00:13:44] We're able to use the tools for agents. [00:13:45] We're able to use the tools for agents. [00:13:46] We're able to use the tools for agents. [00:13:47] We're able to use the tools for agents. [00:13:50] We're able to use the tools for agents. [00:13:51] We're able to use the tools for agents. [00:13:53] And we're able to use the tools for agents. [00:13:55] We're able to use the tools for agents. [00:13:56] We're able to use the tools for agents. [00:13:57] We're able to use the tools for agents. [00:13:59] We're able to use the tools for agents. [00:14:00] We're able to use the tools for agents. [00:14:03] We're able to use the tools for agents. [00:14:04] We're able to use the tools for agents. [00:14:05] We're able to use the tools for agents. [00:14:06] We're able to use the tools for agents. [00:14:07] We're able to use the tools for agents. [00:14:08] We're able to use the tools for agents. 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[00:14:49] We're able to use the tools for agents. [00:14:50] We're able to use the tools for agents. [00:14:51] We're able to use the tools for agents. [00:14:53] We're able to use the tools for agents. [00:14:54] We're able to use the tools for agents. [00:14:55] We're able to use the tools for agents. [00:14:56] We're able to use the tools for agents. [00:14:57] We're able to use the tools for agents. [00:14:58] We're able to use the tools for agents. [00:14:59] We're able to use the tools for agents. [00:15:00] We're able to use the tools for agents. [00:15:01] We're able to use the tools for agents. [00:15:02] We're able to use the tools for agents. [00:15:03] We're able to use the tools for agents. [00:15:04] We're able to use the tools for agents. [00:15:05] We're able to use the tools for agents. [00:15:06] We're able to use the tools for agents. [00:15:07] We're able to use the tools for agents. [00:15:08] We're able to use the tools for agents. 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[00:15:29] We're able to use the tools for agents. [00:15:31] We're able to use the tools for agents. [00:15:32] We're able to use the tools for agents. [00:15:33] We're able to use the tools for agents. [00:15:34] We're able to use the tools for agents. [00:15:35] We're able to use the tools for agents. [00:15:36] We're able to use the tools for agents. [00:15:37] We're able to use the tools for agents. [00:15:38] We're able to use the tools for agents. [00:15:39] We're able to use the tools for agents. [00:15:40] We're able to use the tools for agents. [00:15:41] We're able to use the tools for agents. [00:15:42] We're able to use the tools for agents. [00:15:43] We're able to use the tools for agents. [00:15:44] We're able to use the tools for agents. [00:15:45] We're able to use the tools for agents. [00:15:46] We're able to use the tools for agents. [00:15:47] We're able to use the tools for agents. [00:15:48] We're able to use the tools for agents. [00:15:49] We're able to use the tools for agents. [00:15:50] We're able to use the tools for agents. [00:15:51] We're able to use the tools for agents. [00:15:52] We're able to use the tools for agents. [00:15:53] We're able to use the tools for agents. [00:15:54] We're able to use the tools for agents. [00:15:55] We're able to use the tools for agents. [00:15:56] We're able to use the tools for agents. [00:15:57] We're able to use the tools for agents. [00:15:58] We're able to use the tools for agents. [00:15:59] We're able to use the tools for agents. [00:16:00] We're able to use the tools for agents. [00:16:01] We're able to use the tools for agents. [00:16:02] We're able to use the tools for agents. [00:16:03] We're able to use the tools for agents. [00:16:04] We're able to use the tools for agents. [00:16:05] We're able to use the tools for agents. [00:16:06] We're able to use the tools for agents. [00:16:07] We're able to use the tools for agents. [00:16:08] We're able to use the tools for agents. [00:16:13] We're able to use the tools for agents. [00:16:14] We're able to use the tools for agents. [00:16:15] We're able to use the tools for agents. [00:16:16] We're able to use the tools for agents. [00:16:17] We're able to use the tools for agents. [00:16:18] We're able to use the tools for agents. [00:16:19] We're able to use the tools for agents. [00:16:20] We're able to use the tools for agents. [00:16:21] We're able to use the tools for agents. [00:16:22] We're able to use the tools for agents. [00:16:23] We're able to use the tools for agents. [00:16:24] We're able to use the tools for agents. [00:16:25] We're able to use the tools for agents. [00:16:26] We're able to use the tools for agents. [00:16:27] We're able to use the tools for agents. [00:16:28] We're able to use the tools for agents. [00:16:29] We're able to use the tools for agents. [00:16:31] We're able to use the tools for agents. [00:16:32] We're able to use the tools for agents. [00:16:33] We're able to use the tools for agents. [00:16:34] We're able to use the tools for agents. [00:16:35] We're able to use the tools for agents. [00:16:36] We're able to use the tools for agents. [00:16:37] We're able to use the tools for agents. [00:16:38] We're able to use the tools for agents. [00:16:39] We're able to use the tools for agents. [00:16:40] We're able to use the tools for agents. [00:16:41] We're able to use the tools for agents. [00:16:42] We're able to use the tools for agents. [00:16:43] We're able to use the tools for agents. [00:16:44] We're able to use the tools for agents. [00:16:45] We're able to use the tools for agents. [00:16:46] We're able to use the tools for agents. [00:16:47] We're able to use the tools for agents. [00:16:48] We're able to use the tools for agents. [00:16:49] The computing pattern, the computing pattern of software is going to change. [00:16:54] In fact, let's come back to this. [00:16:56] This is the agent. [00:16:58] It is the ultimate disaggregated and distributed computing, computing model. [00:17:07] So many different computers are going to be activated in order to process this agent. [00:17:12] The agent consists of model, harness, tools and skills, and a runtime. [00:17:25] All of that is running at different places in a data center. [00:17:29] You can think of the model as the brain, the harness as the body, the tools that it uses, [00:17:39] working in a runtime. [00:17:42] Think of it as a workshop. [00:17:44] So this is a person, a worker working with tools in a workshop. [00:17:49] Of course, this is being done at extraordinarily large scales. [00:17:53] And each one of those steps are running in a different part of the computer. [00:17:58] And you could see the large language model is thinking, context processing, observing, [00:18:05] understanding the environment, reasoning, coming up with a plan, and acting on the plan. [00:18:12] Every single time that happens, an entire rack of Grace Blackwell NVLink 72 is activated. [00:18:20] It's thinking with the large language model. [00:18:23] Whenever it uses a tool, a CPU is used. [00:18:27] That tool could be a C compiler. [00:18:30] It could be Python. [00:18:31] It could be JavaScript. [00:18:33] Or it could be accelerated computing. [00:18:36] Today's agents are relatively simple users of tools. [00:18:41] Tomorrow, they're going to be very sophisticated users of tools, which is the reason why the CUDAX libraries that I showed you are going to be incredibly popular with agents. [00:18:50] They solve some of the most important problems the world knows. [00:18:53] And all of our CUDAX libraries are now going to come with skills that the AI could learn how to use. [00:19:03] So the CUDAX library, some skills, basically a manual. [00:19:08] The AI reads it and go, aha, that's how you use it. [00:19:11] The ability to use these libraries by agents are going to be incredible. [00:19:18] And so the tools run on CPUs and GPUs and large language models. [00:19:23] The security harness runs on CPUs and a security processor called the DPU, NVIDIA's Bluefield. [00:19:32] The orchestration of all this runs on a CPU. [00:19:35] This is the entire harness, and the CPU is orchestrating all of the work. [00:19:40] One of the hardest parts is memory. [00:19:43] You could just imagine. [00:19:44] The working memory is called KV caching. [00:19:47] What to remember, compaction, not just compression, but how to retrieve. [00:19:53] Do you retrieve structured data? [00:19:55] Do you retrieve unstructured data? [00:19:57] What is the ontology, the relationship of all of these different data to itself? [00:20:03] That entire processing is incredibly complicated. [00:20:07] The memory system, the memory system of AIs is going to cause the storage system to be completely revolutionized. [00:20:16] As you could see, every aspect of this computing model, this computing pattern, this new application called an agent, is fundamentally different than the way that applications used to run. [00:20:30] A whole bunch of software sitting inside a binary, sitting inside an operating system. [00:20:35] This is the reason this disaggregated, this distributed, this heterogeneous computing problem is precisely the reason we built our next generation. [00:20:49] Verirubin. [00:20:50] Verirubin. [00:20:51] Verirubin is not one chip. [00:20:54] Verirubin is not a GPU only. [00:20:57] It starts with the GPU. [00:20:59] But Verirubin is not a GPU. [00:21:00] But Verirubin is incredible. [00:21:03] This entire thing is Verirubin. [00:21:07] From end to end. [00:21:09] It has GPUs. [00:21:11] Verirubin, NVLink 72. [00:21:14] It is orchestrated by Verirubin CPUs that I'm going to tell you more about. [00:21:18] The storage systems. [00:21:20] Revolutionary. [00:21:21] VERA, along with CX9, our software stack called DOCA, the security processor that's inside so that everything is encrypted at rest, in motion, as well as in use. [00:21:38] Everything across this is secure because the AI model is so precious. [00:21:43] This is the reason why this entire system obeys confidential computing. [00:21:48] Each one of these systems would be a complete revolution in itself. [00:21:53] VERA-RUBEN is the most ambitious endeavor in the history of our company. [00:21:59] The whole company worked on VERA-RUBEN across all 40,000 engineers, not to mention all of you. [00:22:07] All of you participated in the creation of this entire system. [00:22:12] VERA-RUBEN is really a miracle, and it's not just one chip. [00:22:16] It is so many. [00:22:18] Well, it's even beyond that. [00:22:20] A long time ago, NVIDIA used to be a GPU company. [00:22:24] But over the years, we've evolved to become a systems company. [00:22:29] You're looking here now for the most complex systems, most complex and ground-up system ever designed. [00:22:37] But ultimately, our customers, our partners, don't want to buy computers. [00:22:44] They want to build AI factories, which is the reason why NVIDIA has really started to transform ourselves yet again. [00:22:52] You could see so much of our technology is now at the entire infrastructure scale. [00:22:58] Our partners are at infrastructure scale. [00:23:01] Power generators, cooling systems, the grid providers. [00:23:06] And so many industrial companies are now part of our ecosystem because ultimately, we're trying to build an entire stack, just like GPUs, just like when we were building Grace Blackwall MVLink 72, just like now. [00:23:22] We are building a full stack system so that our customers could build amazing AI infrastructure. [00:23:30] Let's take a look. [00:23:33] The world is racing to build AI factories, the largest infrastructure build-out in human history. [00:23:40] AI factories are incredibly complex. [00:23:42] Every layer, chip, rack, network, power, cooling, grid, must be designed together from end to end because compute is revenues. [00:23:55] NVIDIA DSX is the blueprint, a reference design for building and operating AI factories at maximum efficiency and profit. [00:24:03] It starts with DSX SIEM, with the DSX SIEM Omniverse Blueprint, partners design and validate an NVIDIA Vera Rubin AI factory before a single rack lands. [00:24:16] They plan the layout, simulate the power and cooling, design the network, validate every integration, test every change in the digital twin. [00:24:32] The factory powers on DSX OS takes over and provisions, operates, monitors, and remediates the infrastructure, turning the installed systems into trusted, multi-tenant, resilient, AI-ready capacity. [00:24:49] Today's AI factories over-provisioned power by up to 40%. [00:24:53] DSX Max LPS lets operators safely deploy more GPUs inside the same power budget, adding billions in annual revenue. [00:25:06] Breakthrough hot liquid cooling at 45 degrees Celsius uses less water and energy. [00:25:12] More power going to revenue-generating compute. [00:25:15] Incredible. [00:25:17] Dynamic power allocation steers power from rack to rack, recovering stranded watts, sending them where work is happening. [00:25:26] In-rack power smoothing flattens peak current spikes and power surges. [00:25:34] Throughout the factory, teams of AI agents work with DSX Max LPS, continuously coordinating to balance cooling and power to meet workload demand. [00:25:45] DSX-AI factories are flexible energy assets that operate cooperatively with the grid. [00:25:51] DSX Flex reads real-time grid signals and dynamically adjusts factory power when the grid needs relief. [00:26:01] A hundred gigawatts of AI factories will come online before the end of the decade. [00:26:06] NVIDIA DSX AI factories run at highest efficiency, produce the lowest cost tokens, and make the grid stronger. [00:26:22] I've shown you ecosystem slides of the past, where NVIDIA's computing layers and software and software and computing stacks are integrated into other people's platforms, third-party platforms and libraries that serve end markets. [00:26:39] That was a computing ecosystem. [00:26:42] This is an AI factory ecosystem. [00:26:45] This is way downstream of all of you. [00:26:48] Upstream of me is all of you, and downstream of us is this ecosystem. [00:26:54] Because NVIDIA ultimately is not just building a GPU, not just building a system. [00:27:00] We're helping customers build these AI factories, these AI infrastructure that is so immensely complex. [00:27:07] Each one of these at one gigawatt level started at $30, $20, $30 billion. [00:27:15] It is at $50, $60 billion, and soon it will be $80, $100 billion per gigawatt. [00:27:24] $100 billion into an AI factory. [00:27:28] It must work the first time, and it must work right away. [00:27:32] The cost of capital is incredible. [00:27:34] The complexity is incredible. [00:27:36] So as you see, we used to design a chip inside a computer, and then we simulated a system inside a computer. [00:27:46] Today, you saw just now, everything was built in Omniverse. [00:27:53] I've been working with Omniverse with all of you for a long time. [00:27:56] This was the dream come true, so that we can build these gigantic systems as large as the world wants to build, inside a digital framework, inside a digital simulator, in a digital world, long before we build the first break ground and put our money to work. [00:28:15] So this is our ecosystem, we call it DSX. [00:28:19] RTX is for our GPU, DGX is for our systems, and now DSX, basically infrastructure. [00:28:27] Because of the work that we do here, across this entire stack, including our systems and software, it's the reason why we can work with small companies and enable them to be world-class AI clouds. [00:28:40] Every one of these I'm about to show you are small companies just recently, and now CoreWeave is worth $50, $60, $70 billion, and growing incredibly fast. [00:28:51] Recently, we worked with Nebius, and again, they're growing incredibly fast. [00:28:55] Each one of these clouds have incredible customers. [00:29:00] Cursor, the software coding company, Black Mountain Labs, Image Generation, World Labs, World Foundation Model, Revolut, the leading financial services AI company, and Shopify. [00:29:13] Here's another one. [00:29:15] This is Nscale, and their customers are British Telecom, Google. [00:29:20] Google is using one of our AI clouds. [00:29:23] Thinking machines, a Frontier Labs company, which is super exciting. [00:29:28] Here's Naver Cloud in Korea, Bank of Korea, Hyundai, so many incredible companies. [00:29:36] Here's one in India, Yoda, incredible companies. [00:29:40] Here's one based in Singapore, building in Australia, Together AI, AI Singapore. [00:29:48] This is one in Indonesia. [00:29:51] Each one of these companies, each one of these companies are serving regional as well as global customers. [00:29:58] AI is going to run everywhere. [00:30:01] Every company will be powered by it. [00:30:03] Every region will build it. [00:30:07] Endostat here in Indonesia. [00:30:09] Here in Taiwan, GMI. [00:30:13] Here in Taiwan, GMI. [00:30:15] It's okay to clap. [00:30:22] So, incredible, incredible, incredible companies, incredible opportunity. [00:30:28] But all of them need several things. [00:30:30] Of course, they need the computing stack. [00:30:32] This entire stack underneath. [00:30:34] This is what made NVIDIA famous. [00:30:37] All of our hardware and software and libraries, our connection into the world's ecosystem of third-party developers, makes it possible for anyone to stand up an AI cloud. [00:30:49] However, the AI cloud is so complex now. [00:30:53] This is the software version. [00:30:55] This is the computer science version. [00:30:58] The money version. [00:31:00] The asset version is what I showed you earlier. [00:31:04] It's a giant factory. [00:31:07] Having this ability alone is not enough, which is the reason why NVIDIA has become an AI infrastructure company. [00:31:14] Now, doing this well and becoming incredibly good at helping customers build AI factories and deploying AI factories is incredibly important. [00:31:26] And the reason for that is this. [00:31:28] Compute is revenue now. [00:31:31] Compute is profit. [00:31:32] The absence of revenues and profit is loss. [00:31:37] And so it's really important to realize that this is when, this is an example of an AI infrastructure coming online. [00:31:47] It could take, it could be coming online quickly. [00:31:51] It could take a while. [00:31:53] Its throughput could be high. [00:31:54] It could be low. [00:31:55] Its resilience and reliability could be good or bad. [00:32:00] And its lifetime of usefulness could be long or short. [00:32:04] Because this represents 50, 60, going to $100 billion. [00:32:13] This curve matters greatly. [00:32:15] Which is the reason why NVIDIA is such a great partner. [00:32:19] Working with us because of our fully integrated capability, we didn't just come up with a PowerPoint slide. [00:32:27] We created the entire infrastructure. [00:32:29] We connected everything together. [00:32:31] We built out billions and billions of it ourselves. [00:32:34] To make sure that everything works well. [00:32:38] As a result of that, our time, our time to first token, our time to first token, our time to first inference, our time to training turned on is much faster. [00:32:53] Second, because our throughput per watt, our tokens per watt, is utterly world class. [00:33:04] And the reason for that is because we integrate everything, we design everything from the ground up, we simulate the entire system, and we use extreme co-design. [00:33:12] Just like I showed you just now with the Vera Rubin rack, everything was designed in order to deliver on this incredible throughput. [00:33:21] If your data center, if your data center, if your factory, has one gigawatt, it will not have more. [00:33:30] One gigawatt means one gigawatt. [00:33:33] That's all the power generation you could do. [00:33:35] If you have one gigawatt of power, then throughput per watt is revenues. [00:33:43] Because every token is profitable. [00:33:46] Every token is revenues. [00:33:49] This is the future. [00:33:51] Compute is revenues. [00:33:53] Performance per watt is your revenues. [00:33:56] Choosing the wrong architecture, just because the chips are cheaper, doesn't translate, doesn't make sense. [00:34:05] You need to make sure that your revenues per watt, the more you buy, the more you make. [00:34:12] And so tokens per watt. [00:34:15] And then lastly, very light, oh, second, third, is reliability. [00:34:20] If you ever get a chance to see these data centers, there are so many moving parts, millions of cables. [00:34:27] The ability for all of those computers to work harmoniously, reliably, is extremely low. [00:34:35] It is just extremely difficult. [00:34:37] We have now been operating very large scale for a very long time. [00:34:41] That experience matters. [00:34:44] That difference, mean time between interrupts, extremely important. [00:34:49] And then lastly, this is very hard. [00:34:53] The lifetime of these systems, the lifetime of these systems, the software is changing all the time. [00:35:00] Four years ago, which is in the time of Hopper, AI has completely changed. [00:35:07] Six years ago, this is the time frame of Ampere, AI has completely changed. [00:35:14] We started out talking about CNNs. [00:35:17] Here we are, then we talked about transformers. [00:35:20] And then we talked about mixture of experts. [00:35:22] Now we're talking about agentic systems. [00:35:25] Every single generation, every single few months, the software industry is coming up with new technology. [00:35:34] If your architecture is not flexible, if your ecosystem is not rich, then this curve cannot be long. [00:35:44] You cannot predict how long your system can last. [00:35:47] You cannot predict how long your system can last. [00:35:48] I can. [00:35:50] NVIDIA systems is all over the world. [00:35:53] Software developers start with NVIDIA CUDA. [00:35:56] And by definition, therefore, the life, the ecosystem, the useful asset is going to be much longer. [00:36:04] The difference is essentially cost. [00:36:07] You could think of it as revenues, but the other side of revenues is cost. [00:36:11] If the life of the asset is long, if the life of the asset is long, the TCO is low. [00:36:17] This is the difference. [00:36:18] This is what it looks like when compute. [00:36:25] The more you buy, the more you make. [00:36:36] Now, all of you are experiencing this with me. [00:36:40] Isn't that right? [00:36:42] All of your demand, your factories are working so hard. [00:36:46] Your people are working so hard all across Taiwan because everybody wants to make money. [00:36:53] They realize that AI, useful AI is here. [00:36:59] Profitable AI is here. [00:37:02] Compute demand is incredibly high. [00:37:06] And compute demand is the constraint. [00:37:08] And so let's go work super, super hard and help the world stand up AI factories everywhere. [00:37:14] This is why it's so important. [00:37:16] I'm so happy. [00:37:18] Here I am standing in front of you. [00:37:21] Vera Rubin is in full production. [00:37:23] Vera Rubin is in full production. [00:37:33] The supply chain we created for Vera Rubin is twice as large as Grace Blackwell. [00:37:42] Yeah, it's incredible. [00:37:44] And, and what used to take two hours to assemble one Grace Blackwell rack now only takes five minutes. [00:37:53] So not only is the capacity higher, the throughput is a lot faster and we need it all to support the demand. [00:38:03] This ecosystem is extraordinary. [00:38:06] Millions of square feet has been put online to support Grace Blackwell and preparing now, ramping up now, Vera Rubin. [00:38:15] I want to thank all of you. [00:38:16] Vera Rubin is now in full production. [00:38:18] Thank you. [00:38:23] Let's take a look. [00:38:27] Large language models generate answers. [00:38:31] Now AI agents can do work. [00:38:33] But processing agentic AI is a whole different kind of problem. [00:38:38] Agents observe, reason, plan, use tools. [00:38:42] They manage massive context, juggling working memory and long-term memory. [00:38:46] They spin up sub-agents, specialists on demand. [00:38:50] NVIDIA Vera Rubin is a multi-rack pod scale system built to process agentic AI and is now in full production. [00:38:58] The manufacturing, automation and orchestration across the supply chain, a miracle to witness. [00:39:04] Our journey started when we launched the first AI supercomputer, NVIDIA DGX1. [00:39:09] Over the next decade, we pushed every chip and system to the limit, from Pascal and the first MVLink, to Grace Blackwell, the first rack scale AI supercomputer, and now Vera Rubin, the first multi-rack pod scale supercomputer built for the agentic age. [00:39:28] It starts at TSMC, the seven new chips that make up Vera Rubin, take shape through hundreds of processing steps. [00:39:35] Three nanometer process, COWAS-R and COWAS-L packaging, HBM4 memory, from Micron, SK Hynix, and Samsung. [00:39:45] The Vera Rubin compute board, six trillion transistors with over 18,000 components on one board. [00:39:52] Vera Rubin MVL72 does the thinking, prompt and context understanding, reasoning, and planning. [00:39:59] Next, a new modular compute tray, streamlined with a new PCB mid-plane design, super chips, Kinect X9 super NICs, and Bluefield 4 DPUs, all made in place, with no cables for resiliency at AI factory scale. [00:40:16] Eighteen compute trays, nine hot swappable MVLink switch trays, new high-efficiency manifolds, liquid-cooled bus bars, carrying over 5,000 amps, the equivalent of 20 electric cars at full acceleration. [00:40:31] Together, 1.3 million components form this third-generation MGX rack design. [00:40:37] Congratulations to Microsoft for their operational Vera Rubin MVL72 engineering rack. [00:40:43] Congratulations to Dell and CoreWeave as well, for standing up their Vera Rubin MVL72 engineering rack. [00:40:50] Then, the Vera CPU rack. [00:40:52] 256 CPUs in a single liquid-cooled rack, orchestrating the models, shuffling memory, launching tools. [00:41:02] At Foxconn and Quanta, GROC3 LPX takes shape. [00:41:06] 256 GROC3 LPUs across 16 trays, 40 petabytes per second of SRAM bandwidth for ultra-low latency. [00:41:15] While MVL72 generates tokens at the highest throughput, GROC LPX generates them at the lowest latency. [00:41:24] Vera Bluefield 4 STX, where AI keeps its memory. [00:41:29] Storage processing accelerated by Bluefield 4, connecting memory, storage, and in-silicon security. [00:41:37] And NVIDIA Spectrum X Ethernet Photonics, the world's first Ethernet switch with 200 gigabit co-packaged optics. [00:41:46] TSMC's coop process, chip-scale packaging, and ultra-high-powered laser dies on indium phosphide. [00:41:54] Vera Rubin, five connected rack-scale systems, a supercomputer for AI agents, 150 supply chain partners across Taiwan, millions of square feet of factory floor, hundreds of sites, chips, packages, systems, and data centers pushed to the limits of size, power, and scale. [00:42:16] This is what we call extreme co-design. [00:42:18] We did this with Taiwan. [00:42:20] Together, we reinvented computing for the age of AI. [00:42:24] Taiwan was with us at the beginning. [00:42:26] And here today, as we bring Vera Rubin to the world. [00:42:30] Thank you, Taiwan. [00:42:39] Ladies and gentlemen, Vera Rubin. [00:42:43] Vera Rubin was not just built for AI. [00:42:48] AI, Vera Rubin was not built just to run AI. [00:42:51] Vera Rubin was built to run agents. [00:42:54] This is an agentic system. [00:42:58] Imagine the complexity, which is the reason why agents is the last computer science breakthrough. [00:43:06] It has taken this many years for agents to realize its potential and become useful. [00:43:11] It stands to reason that the computer that runs it is the most advanced in the world. [00:43:16] This is Vera Rubin. [00:43:18] Let's take a look. [00:43:19] Can we bring out Vera Rubin, please? [00:43:24] And Janine, do we have the racks, the systems? [00:43:43] It looks heavy. [00:43:52] This is Vera Rubin. [00:43:54] Vera Rubin MVLink 72. [00:43:57] This is the Grok LPX. [00:44:00] At the next GTC, I'm going to talk to you about a lot more of this. [00:44:04] Today, we have so much to talk to you about. [00:44:07] This is Vera CPU rack, 256 CPUs, all liquid cooled. [00:44:13] Let me tell you about Vera in just a moment. [00:44:15] This is the Vera Bluefield storage processing system and also security system. [00:44:23] And, of course, this is our Mellanox networking, the world's first CPO. [00:44:30] This is Vera Rubin. [00:44:32] Incredible technology all coming together. [00:44:35] Now, when we built Hopper, we built Hopper, as you know, for pre-training. [00:44:41] Pre-training was the most important application, the most important workload we were working on at the time. [00:44:47] Then when we worked on Grace Blackwell, everybody said, Jensen, you know, NVIDIA is really good at pre-training. [00:44:55] Inference is so easy. [00:44:57] Do you remember that? [00:44:59] People used to say, inference is so easy. [00:45:01] We could do that, too. [00:45:03] But as you know, inference equals money. [00:45:06] And the models, MOEs, are so complicated. [00:45:10] And to do it at incredibly high response time, fast interactivity, and high throughput at the same time is incredibly hard, which is the reason why we created NVLink 72. [00:45:23] Today, NVIDIA's token cost is the lowest in the world. [00:45:28] Not by 10%, by X factors, orders of magnitude. [00:45:33] All because we did extreme co-design, all because we understood the computing model, the computing pattern of inference, and we were able to create NVLink 72. [00:45:45] Now, with Vera Rubin, it is beyond inference. [00:45:50] It is now inference in an agentic system. [00:45:54] This is Vera Rubin. [00:45:57] No cables, no hoses, no fans. [00:46:03] What used to take the last time when I showed this to you, we had cables everywhere. [00:46:09] The cables were amazing to look at. [00:46:12] But now, there's a PCB in the middle, which connects both sides. [00:46:17] What used to take two hours, now takes five minutes. [00:46:21] The reliability and the resilience of Vera Rubin is going to be off the charts. [00:46:27] This is our Vera CPU tray. [00:46:30] The most advanced CPUs that has ever been built. [00:46:34] I'm going to show you that in just a second. [00:46:37] And this is our storage tray. [00:46:41] Two Vera CPUs, four CX9, incredible amounts of software. [00:46:49] This is our new LPX, LPU30, the GROC system, designed for very low latency inference. [00:46:58] The throughput is delivered by Vera Rubin and extended with NVLink 72. [00:47:04] If you want to extend that even further, you can have GROC LPUs. [00:47:11] Here, we have the Vera Rubin NVLink, the switch tray. [00:47:15] This is the switches in the middle, and this is revolutionary. [00:47:20] Because of Vera Rubin's, because of NVLink 72 and the NVLink switches that we created and invented. [00:47:27] And this is our Ethernet switches for scale-out. [00:47:33] What's amazing is we introduced these two systems for Grace Blackwell. [00:47:40] These two systems were created for Grace Blackwell. [00:47:43] And today, NVIDIA is the largest networking company in the world. [00:47:48] I'm so proud of the networking team. [00:47:51] This is such an incredible enabler for everything that we do. [00:47:56] I'm going to now talk to you about the next major industry we're going to be part of. [00:48:02] Thank you, Janine. [00:48:05] Thank you. [00:48:13] I think there are 2,000 people back there pulling that. [00:48:25] Okay, let's talk about CPUs. [00:48:31] Vera CPUs. [00:48:33] CPUs built for the age of AI. [00:48:35] All of the CPUs until now were created for people. [00:48:42] We were the users. [00:48:44] We were the users. [00:48:45] We were the renters. [00:48:47] The way we use CPUs, we live in a world counted by seconds. [00:48:53] The way we rent CPUs in the cloud, each one of them, the more CPU cores you have, the more you can rent. [00:49:02] The use case of the old CPU and the economics of the old CPU is fundamentally different than agents. [00:49:12] Agents are impatient. [00:49:15] They don't live in a world that is in seconds. [00:49:17] They live in a world that's in nanoseconds. [00:49:20] When it uses a tool, it wants the response time to be as fast as possible. [00:49:27] When it accesses database, it has to come back as soon as possible. [00:49:32] Every moment that the agent is waiting keeps it from going to the next step, the next step, the next step. [00:49:40] It is vital that we make the CPUs as low latency as possible, as interactive as possible. [00:49:49] So we created Vera CPU for the age of AI. [00:49:53] Now, inside our system, it's used for three different ways. [00:49:57] The first way, of course, is Vera Rubin for thinking. [00:50:04] And inside the Vera Rubin rack, there are already two CPUs. [00:50:09] As you know, we are building and selling millions of Vera Rubins. [00:50:16] We have sold millions of Grace Blackwells. [00:50:20] NVIDIA already is one of the largest CPU makers in the world. [00:50:24] In the Vera Rubin rack are two CPUs. [00:50:27] One, for orchestrating and managing the GPUs, managing the KV cache, [00:50:35] dealing with all of the software that runs in the rack. [00:50:40] We also have the Grace Bluefield that is used for security and isolation. [00:50:46] The Vera Compute is used for the harness, the orchestration of the AI models, tool use, accessing the database. [00:50:56] And the data servers are right here, Vera Bluefield, the fastest storage servers, the fastest storage system the world has ever made. [00:51:08] And the reason why this is so vital is because agents are accessing memory, accessing memory so incredibly fast. [00:51:16] These systems, the storage server and the CPUs, are now the critical path of the most expensive part of the data center. [00:51:27] This is the most expensive for a good reason. [00:51:30] This is the most expensive for a good reason. [00:51:32] The economics, the economics of the AI factory is tokens. [00:51:37] And the tokens are created here. [00:51:40] And so, of course, you want to manufacture and generate as many tokens as possible. [00:51:47] This is where you put all of your economics. [00:51:49] And this has to not be in the way. [00:51:52] And so, Vera CPU has great pressure on the Vera, on the CPU architecture, which is the reason why we built a brand new architecture from the ground up. [00:52:02] A CPU the world has never seen before. [00:52:05] We call it Vera. [00:52:08] This is CPU for agents. [00:52:12] All the CPUs of the past, we built for humans. [00:52:16] This CPU is built for agents. [00:52:19] Well, there are four things to keep in mind. [00:52:21] The four takeaways. [00:52:23] The first takeaway is that the instructions per clock of Vera has to be incredibly good because we need the latency to be short. [00:52:33] We need the processing time, single-threaded performance, not throughput, single-threaded performance has to be world-class, absolutely the best. [00:52:43] Single-threaded performance, which is the reason why the IPC, the instructions per clock of Vera, is so high. [00:52:51] It's the highest in the world. [00:52:52] Ten instructions fetched, decoded, and executed per clock, number one. [00:52:59] Number two, the bandwidth necessary to move data in and out for the CPU has to be utterly world-class. [00:53:08] The second thing is bandwidth per core. [00:53:12] The third is just bandwidth, period. [00:53:15] We're moving. [00:53:16] Remember, I said earlier, agentic systems is fundamentally disaggregated and distributed. [00:53:25] When computing is disaggregated and distributed, networking becomes the problem. [00:53:33] Therefore, we have to move the data around as fast as possible between the CPU cores and between the CPU and the storage, the CPU and the GPU. [00:53:43] The bandwidth around the system and inside the CPU core has to be utterly world-class. [00:53:50] This is the first CPU that's been built a long time that is literally at radical limits with a fabric that connects all of the CPU cores that is speed of light. [00:54:02] 3.6 terabytes per second. [00:54:06] No chiplet tacks, no chip boundary crossings, because we need to have everything, because the CPU cores are talking to each other with extremely high bandwidth. [00:54:18] They're not rented core per core per core. [00:54:21] They're all working together. [00:54:23] The cross-sectional bandwidth of Vera is off the charts. [00:54:26] It's the first one to be PCI Express Gen 6. [00:54:30] It is also the first one to have LPDDR5 with 1.2 terabytes per second. [00:54:38] Three times, two to three times the bandwidth of the highest performance CPUs on the outside, three times the bandwidth on the inside. [00:54:48] The bandwidth per core and the bandwidth period is world-class. [00:54:53] Now, remember, I showed you earlier, the number of CPU cores, the number of CPUs is going to be quite high. [00:55:01] And the reason for that is very simple. [00:55:04] We created CPUs for humans in the past. [00:55:09] And humans, there are only one billion of us. [00:55:13] There will be billions of agents, and these agents are going to be using the CPUs with very little patience, because the cost of the GPU they sit next to is too high. [00:55:26] And therefore, too valuable, too precious. [00:55:31] Therefore, these CPUs are going to be both performant, but they also have to be extremely energy efficient so that we can cram as much CPU as we can into the factory without taking away power from the token generation, which we know is how we make money. [00:55:52] These four properties, instructions per clock or single-threaded performance, bandwidth per core, the total bandwidth around the chip and inside the chip, and energy efficiency defines Vera. [00:56:07] It is absolutely world-class. [00:56:10] When you compare it to the highest-performance x86, it is just off the charts. [00:56:15] When you compare it in real, single-threaded performance, real performance, it's off the charts. [00:56:23] It is incredible to be able to deliver 5% improvement on CPUs. [00:56:29] It is incredible to be able to deliver 10%. [00:56:31] But this kind of performance speed-up is just unheard of. [00:56:36] This is NVIDIA Vera. [00:56:40] What do you think? [00:56:47] Let's take a look. [00:56:49] Agentic AI changes the role of the CPU. [00:56:53] The CPU is now the conductor, and the GPU is the orchestra. [00:56:57] Traditional CPUs were built for a different era, maximizing cores per socket. [00:57:03] Slice them up, virtualize, rent by the hour. [00:57:06] In the age of agents, the CPU is now a bottleneck to GPU utilization, directly affecting token throughput, latency, and user experience. [00:57:18] NVIDIA Vera is the CPU built for the agentic loop, combining NVIDIA's custom data center CPU core with the scalable coherency fabric for the right balance of performance cores and bandwidth to maximize AI factory output. [00:57:32] At the heart of Vera is the NVIDIA Olympus core, built for modern data center workloads, branch-heavy Python runtimes, tool calls, and sandbox code execution. [00:57:43] Each core is tuned for throughput. [00:57:44] Each core is tuned for throughput, a neural branch predictor evaluating two taken branches per cycle, a 10-wide decode engine brings in more work each cycle, a large out-of-order engine keeps instructions moving, advanced prefetchers with a novel graph engine anticipating the next data path. [00:58:02] But fast cores only matter when data arrives correctly and on time. [00:58:07] Vera is the first CPU to use LPDDR5X memory, while correcting multiple errors simultaneously without compromising bandwidth. [00:58:17] Vera achieves 40% lower peak memory latency versus x86, keeping cores fed on time through retrieval, analytics, and sandbox execution. [00:58:27] NVIDIA's second-generation scalable coherency fabric unifies all 88 Olympus cores on a monolithic mesh with separate dies for memory and I/O. [00:58:38] Cores are not split across chiplets, enabling 50% faster core-to-core communication than traditional CPUs. [00:58:46] And memory-coherent NVLink chip-to-chip connects GPUs directly to the fabric. [00:58:53] Beyond GPUs, NVLink chip-to-chip can scale Vera up to multiple sockets, enabling massive bandwidth between CPUs. [00:59:02] Vera delivers 1.8 times the agentic sandbox performance of x86 CPUs. [00:59:08] Standalone Vera racks run agent sandboxes, tools, code, and data pipelines. [00:59:15] Tightly coupled to Reuben GPUs, Vera keeps accelerated workflows moving. [00:59:20] NVIDIA Vera Bluefield 4 STX powers context memory and AI storage. [00:59:27] Compute, networking, storage. [00:59:30] Vera is the CPU for the age of agents. [00:59:35] This is going to be our new major growth driver. [00:59:45] The reviews are already coming out. [00:59:47] And it's pretty good. [00:59:49] That's pretty good stuff. [00:59:52] Now remember, Grace and Vera are also the most highly qualified CPUs in the world of AI, because every single data center, every single cloud, every single enterprise, every company that works with NVIDIA on AI has already qualified. [01:00:20] Grace, the entire software stack has already been optimized for Grace. [01:00:26] Every company will be qualifying Vera. [01:00:29] Vera will be the most optimized agentic CPU in the world, simply because it's going to go with Vera Rubin, simply because we made the big hard switch. [01:00:41] In fact, during Grace Blackwell transition, the biggest risk was going from external CPU x86 into Grace Blackwell. [01:00:51] That transition was extremely dangerous. [01:00:54] But we did it with incredible execution. [01:00:57] Now Grace is literally synonymous with Grace Blackwell. [01:01:02] When people say Blackwell, they say Grace Blackwell, because it is utterly now everywhere. [01:01:07] Every company's software stack has been optimized for it. [01:01:10] Everybody's security stack has been optimized for it. [01:01:13] And now here comes Vera. [01:01:15] I'm super excited about that. [01:01:16] Now look at some of the performance numbers. [01:01:20] Speed ups is one thing. [01:01:21] It is extremely hard to speed up SQL. [01:01:26] SQL, the most famous domain specific language DSL that has ever been created. [01:01:36] Before SQL, you know, before CUDA, there was SQL. [01:01:41] Before OpenGL, there was SQL. [01:01:43] Invented by IBM. [01:01:45] Today, it is the structured database engine of the planet. [01:01:49] Everybody uses SQL. [01:01:51] This is SQL running three times faster. [01:01:55] Not 10% faster, not 25% faster. [01:01:58] Ten times, three times faster. [01:02:01] Incredible. [01:02:02] This is real time. [01:02:06] The next one is real time stream processing. [01:02:09] Remember, your AI is going to be not just reading documents. [01:02:14] Your AI is going to be watching for telemetry, especially inside a factory, inside a stock exchange. [01:02:22] You're going to be looking for telemetry continuously. [01:02:25] The burst of data that's coming in goes into a CPU. [01:02:29] This is Vera CPU running real time stream processing for New York Stock Exchange. [01:02:35] Lynn Martin, the president of New York Stock Exchange, has been so gracious to partner with us. [01:02:41] This system is run all over the world in real time stream processing. [01:02:46] Vera CPU six times, all because of the bandwidth, the single threaded instruction execution, the bandwidth inside between the cores, the bandwidth outside. [01:02:58] Vera is completely revolutionary. [01:03:01] That's Vera. [01:03:02] You know, X factors is something you talk about when you're talking about GPUs. [01:03:14] It is quite rare that somebody talks about X factors on real workload, real workload that is associated with CPUs. [01:03:22] So I'm so proud of the team. [01:03:24] You guys did such a great job. [01:03:25] We have an extraordinary roadmap coming. [01:03:27] But what's really exciting is almost everybody is supporting Vera. [01:03:34] They're as excited as we are. [01:03:36] This is Vera opening up. [01:03:39] It's opened up a brand new market. [01:03:42] Agents. [01:03:43] Agents is a new workload. [01:03:46] We built CPUs for humans in the past. [01:03:49] We need CPUs for agents, agentic systems. [01:03:54] Their properties are different. [01:03:55] Why would the old CPUs be the same? [01:03:57] We are building millions and millions of errors. [01:04:02] Millions of errors. [01:04:04] And to go to market with us, Taiwan's ODMs and computer makers, all the OEMs, and you could see the early adopters. [01:04:16] The early adopters are the agentic companies. [01:04:19] This is the beginning of a new market. [01:04:22] A market that never existed before. [01:04:25] It's not going to take away from the old markets. [01:04:27] But this is a new market. [01:04:29] CPU for agents. [01:04:32] And this market will surely be larger than the last. [01:04:37] And the reason for that is because there will be a lot more agents than there are people. [01:04:41] And then the agents are very impatient. [01:04:44] So, NVIDIA, Vera, CPU. [01:04:47] Thank you. [01:04:54] This is the most important slide, really. [01:04:57] This is the takeaway. [01:04:58] The takeaway here is that this is the application pattern. [01:05:02] This is the computing pattern of the next decade. [01:05:07] Agents, harnesses, orchestrating large language models. [01:05:13] Every company will run it. [01:05:16] Every company will be an agent company. [01:05:19] Every company will have agents running inside. [01:05:23] Every company will see that agents will need its own operating system. [01:05:30] Every company is asking us, how do we run agents safely? [01:05:34] How do we build agents for our own workloads? [01:05:38] And so, we have the NVIDIA Agent Toolkit for Enterprise AI. [01:05:45] You've seen me build this in plain sight. [01:05:48] Almost everything that NVIDIA does, as you know, at every GTC. [01:05:51] If you go back and look at my GTC five years ago or ten years ago, you will see today. [01:05:56] This you've seen me talking about for several years now because we've been building for this moment. [01:06:04] There are four things that companies need in order to build agents as a service or build agents to operate. [01:06:13] The first thing you need is you need models. [01:06:16] Of course, large language models. [01:06:19] The smarter the better, the cheaper the better, the faster the better. [01:06:23] The second is you need a harness to orchestrate the whole thing. [01:06:28] The third, these models want to use tools. [01:06:33] And these tools come with its skills. [01:06:35] And I showed you CUDAX libraries. [01:06:38] Those are going to be amazing tools for the agents in the future. [01:06:42] And then lastly, you need a runtime. [01:06:45] You need the operating system that holds it all together. [01:06:48] This is the NVIDIA Toolkit for agents. [01:06:53] It includes models that you can modify. [01:07:00] NVIDIA's world-class open models. [01:07:02] And I want to show you more. [01:07:03] You can run agents from anybody. [01:07:07] You can run Cloud Code, Incredible Agent. [01:07:10] You can run it inside this harness called Open Shell, which will be highly secure for your inside the enterprise. [01:07:20] The shell protects the agent, keeps it grounded in security policies. [01:07:27] Privacy is protected. [01:07:29] Its rights and privileges are given. [01:07:32] Its identities protected. [01:07:34] And so this Open Shell is being adopted all over the world. [01:07:38] NVIDIA Open Shell is open source. [01:07:40] NVIDIA Open Shell. [01:07:41] You're going to see so many companies adopted. [01:07:42] You're going to see so many companies adopted. [01:07:43] Red Hat. [01:07:44] Canonical. [01:07:45] Microsoft. [01:07:46] It's going to be adopted everywhere. [01:07:48] This is an important. [01:07:50] This is the runtime. [01:07:52] And this runtime is fully optimized for the NVIDIA AI platform, which is everywhere. [01:07:58] So you can run Open Shell in any cloud, on prem, and even on device. [01:08:05] So you have now tools and libraries that they can use. [01:08:10] You have models that you can modify or use as is. [01:08:14] Or you have agents. [01:08:16] This would be Open Claw, Hermes, another incredible harness. [01:08:23] These agentic harnesses can now run on prem or for you anywhere. [01:08:29] Okay. [01:08:30] So four things. [01:08:32] And this represents the operating system of the modern enterprise. [01:08:36] Now, how do we use this? [01:08:38] One of my favorite use cases of agents is chip designers. [01:08:44] It is the single most important thing that NVIDIA does. [01:08:48] And so, of course, we have to partner with Cadence. [01:08:51] To build super agent. [01:08:54] A chip design super agent. [01:08:57] It is orchestrated by codex or cloud code. [01:09:02] It has RTL and architecture diagrams or schematics or specifications as input. [01:09:09] And whatever you need to fix. [01:09:11] And together we created some super agents. [01:09:16] That are optimized for the NVIDIA runtime. [01:09:19] With NEMOTRON. [01:09:22] And let's take a look. [01:09:23] It's really incredible. [01:09:27] Cadence and NVIDIA are partnering to build chip design agents. [01:09:32] Hundreds of thousands of NVIDIA chips come together to make the AI factories that power the world's frontier AI models. [01:09:39] Designing these chips and the systems they run in is one of the hardest engineering challenges. [01:09:45] Trillions of transistors. [01:09:47] Three dimensional circuits. [01:09:49] Microscopic scale. [01:09:50] Every gate. [01:09:52] Every wire. [01:09:53] Synchronized to picoseconds. [01:09:55] Must work in perfect harmony. [01:09:57] With no margin for error. [01:09:58] Physical prototypes are too slow and too costly. [01:10:01] So engineers work in the digital realm. [01:10:04] Each chip begins as a set of architectural specifications. [01:10:07] Then translated into RTL. [01:10:09] The language of chip design. [01:10:11] RTL must be verified in simulation. [01:10:14] A single bug can delay a chip by months. [01:10:17] At NVIDIA, thousands of engineers. [01:10:20] Billions of compute hours per year. [01:10:22] Millions of tests. [01:10:23] Written, run, and debugged. [01:10:25] A cycle that takes teams weeks. [01:10:27] To compress this cycle, Cadence and NVIDIA built a design verification agent. [01:10:32] Codex orchestrates the process. [01:10:36] Cadence ChipStack launches the RTL verification loop. [01:10:40] Powered by Nematron and secured by NVIDIA OpenShell. [01:10:44] Calling on expert sub-agents in RTL generation, test bench creation, regression testing, and debug. [01:10:51] The system drives itself. [01:10:53] The ChipStack agents run hundreds of simulations with Cadence Exilium. [01:10:57] Formal verification with Jasper. [01:11:00] Design flaws revealed. [01:11:02] Bugs in the code fixed. [01:11:04] What once took weeks, now takes hours. [01:11:07] Verification cycles over 40 times faster. [01:11:11] Together, NVIDIA and Cadence are reinventing chip design with AI agents. [01:11:19] From weeks, from weeks to hours. [01:11:22] From weeks to hours. [01:11:24] From weeks to hours. [01:11:25] From weeks to hours. [01:11:26] NVIDIA has thousands of chip designers. [01:11:28] We are going to hire hundreds of thousands of Cadence super agents. [01:11:34] That work with us. [01:11:35] So that we can accelerate our company. [01:11:37] So that we can be even more ambitious. [01:11:40] Create even more amazing things. [01:11:42] Run even faster. [01:11:43] You saw earlier. [01:11:45] That the toolkit. [01:11:46] The toolkit. [01:11:47] With models. [01:11:48] Harness. [01:11:49] Tools. [01:11:50] The tools in this case are Cadence simulators and verifiers. [01:11:55] Formal verification systems. [01:11:57] It is the reason why we're working with Cadence so hard. [01:12:00] To accelerate all of their tools on CUDA. [01:12:03] Accelerator. [01:12:04] Because the agents are impatient. [01:12:06] The agents want the answer immediately. [01:12:08] And so. [01:12:09] Models. [01:12:10] Harnesses. [01:12:11] Accelerated CUDA. [01:12:12] Accelerated libraries and tools. [01:12:13] And then the runtime. [01:12:14] What you saw just now. [01:12:15] Is all of that coming together. [01:12:16] Now one of the things that it starts with. [01:12:18] Is a great model that Cadence could modify. [01:12:22] And tune. [01:12:23] To be expert at the Cadence workflow. [01:12:25] At the Cadence expertise. [01:12:27] So that they could create. [01:12:28] Super agents. [01:12:29] That are proprietary to Cadence. [01:12:31] With their proprietary knowledge. [01:12:33] They have to start with an excellent model. [01:12:35] We call it Nemo Tron. [01:12:37] NVIDIA is dedicated to the technology. [01:12:39] That's a great model that Cadence could modify. [01:12:41] And tune. [01:12:42] To be expert at the Cadence workflow. [01:12:43] At the Cadence expertise. [01:12:44] So that they could create. [01:12:45] Super agents. [01:12:46] That are proprietary to Cadence. [01:12:47] NVIDIA is dedicated. [01:12:48] To build. [01:12:49] Open models. [01:12:50] For the world. [01:12:51] So that all of you. [01:12:52] All of us. [01:12:53] Could create our own agents. [01:12:55] Today we're announcing. [01:12:58] The Nemo Tron. [01:13:00] Three. [01:13:01] Ultra. [01:13:02] Yep. [01:13:03] Our next open model. [01:13:05] And it is smart. [01:13:07] The Nemo Tron. [01:13:13] Models. [01:13:14] Not only give you. [01:13:15] The model. [01:13:16] The model. [01:13:17] We give you. [01:13:18] All the data. [01:13:19] That we use. [01:13:20] To train the model. [01:13:21] And because we have. [01:13:22] A coalition. [01:13:23] Incredible partners. [01:13:24] You can see. [01:13:25] All of our partners. [01:13:26] Down here. [01:13:27] We work together. [01:13:28] Contribute. [01:13:29] Data. [01:13:30] To each other. [01:13:31] Nemo Tron. [01:13:32] Is trained. [01:13:33] On one of the largest. [01:13:34] Suites. [01:13:35] Of long running. [01:13:36] Reasoning models. [01:13:37] Long running. [01:13:38] Tool. [01:13:39] Task. [01:13:40] Tool. [01:13:41] Using. [01:13:42] Data sets. [01:13:43] In the world. [01:13:44] Because of all of our great partnerships. [01:13:46] All of this. [01:13:47] From. [01:13:48] The model. [01:13:49] The training script. [01:13:51] And the data. [01:13:52] Made. [01:13:53] Completely available to you. [01:13:54] This is. [01:13:55] Open models. [01:13:56] At its best. [01:13:57] The best. [01:13:58] Open model system. [01:13:59] Policies. [01:14:00] In the world. [01:14:01] Simple goal. [01:14:02] Is so that you. [01:14:03] Can take all of it. [01:14:04] Add to it. [01:14:05] Make it even better. [01:14:06] Make it yours. [01:14:07] Nemo Tron. [01:14:08] Three. [01:14:09] Ultra. [01:14:10] Is. [01:14:11] Five times faster. [01:14:13] This is the world's first model. [01:14:15] Based on. [01:14:16] A hybrid architecture. [01:14:17] Of. [01:14:18] SSM. [01:14:19] State space models. [01:14:20] With. [01:14:21] Mixture of experts. [01:14:23] The architecture. [01:14:24] Is incredibly fast. [01:14:26] We made a fast. [01:14:27] So that you could think fast. [01:14:28] When you think fast. [01:14:29] You could think longer. [01:14:30] At the same cost. [01:14:32] So five times. [01:14:34] Faster. [01:14:35] It is also. [01:14:37] 30% cheaper. [01:14:39] 30% lower cost. [01:14:40] To run in total. [01:14:41] Flops. [01:14:42] And total inference time. [01:14:43] Than even the most. [01:14:44] Cost effective. [01:14:45] In the world. [01:14:46] We're comparing against. [01:14:48] The world's best. [01:14:49] Open models. [01:14:50] Frontier smart. [01:14:52] Five times faster. [01:14:54] 30. [01:14:55] 30% cheaper. [01:14:57] Completely open. [01:14:59] One. [01:15:00] We're completely. [01:15:01] Dedicated to this. [01:15:02] This is now. [01:15:03] Nemo Tron three. [01:15:04] We're currently working on. [01:15:05] Nemo Tron four. [01:15:06] So this entire toolkit. [01:15:08] From models. [01:15:10] Harnesses. [01:15:11] Tools and skills. [01:15:14] And run times. [01:15:16] Is the reason why. [01:15:17] Every. [01:15:18] Enterprise company in the world. [01:15:20] Has the ability now. [01:15:21] To create their own agents. [01:15:23] Just like. [01:15:24] Cadence did with their super agents. [01:15:26] And we're working with so many companies. [01:15:28] Cadence and crowd strike. [01:15:29] And the soul. [01:15:30] And Palantir. [01:15:31] S.A.P. [01:15:32] And service now. [01:15:33] People were. [01:15:34] Always said. [01:15:35] Jensen. [01:15:36] The agents are going to disrupt these markets. [01:15:39] I said. [01:15:40] Completely opposite. [01:15:41] And you can now see it. [01:15:43] Agents. [01:15:44] Is going to create the largest opportunity. [01:15:46] Ever. [01:15:47] For my partners and friends. [01:15:49] And we have the Nemo. [01:15:50] The. [01:15:51] The. [01:15:51] The. [01:15:52] NVIDIA. [01:15:53] Agentic toolkit. [01:15:54] For enterprise AI. [01:15:55] To help them. [01:15:58] So. [01:15:59] There you go. [01:16:04] First. [01:16:05] Vera Rubin. [01:16:06] In full production. [01:16:07] Two. [01:16:08] Vera CPU. [01:16:09] CPU. [01:16:10] Built for a new generation. [01:16:12] For agents. [01:16:13] And three. [01:16:14] NVIDIA's. [01:16:15] Enterprise AI. [01:16:17] Toolkits. [01:16:18] So that. [01:16:19] Every enterprise. [01:16:20] And every enterprise software company. [01:16:22] Can build agents. [01:16:35] My relationship with you. [01:16:36] Started here. [01:16:39] And many of you. [01:16:40] Many of you. [01:16:41] Many of my friends and partners here in Taiwan. [01:16:43] Your companies. [01:16:45] Started here. [01:16:46] This is in a lot of ways. [01:16:49] The beginning. [01:16:50] Of the modern. [01:16:52] Computer industry. [01:16:53] Forty years now. [01:16:54] NVIDIA's 33 years old. [01:16:56] The PC industry. [01:16:57] Was already starting to get to. [01:16:59] Windows 1. [01:17:00] And Windows 2. [01:17:01] And. [01:17:02] Apple. [01:17:03] Apple 1. [01:17:04] And. [01:17:05] Apple 2. [01:17:05] And. [01:17:06] By the time that we came along. [01:17:07] Windows 3.1. [01:17:08] Was the PC. [01:17:11] And. [01:17:12] As you know. [01:17:13] Windows 95. [01:17:14] Made PC. [01:17:15] Personal. [01:17:16] It took PC. [01:17:17] From. [01:17:18] Enterprises. [01:17:19] Companies. [01:17:20] And. [01:17:21] Made it into a consumer electronics device. [01:17:23] Everybody should have one. [01:17:24] And. [01:17:25] Everybody does. [01:17:26] This is the beginning. [01:17:27] This computing platform. [01:17:28] Did several things incredibly smart. [01:17:30] Windows was not just disaggregated. [01:17:31] As you know. [01:17:32] Windows. [01:17:33] Was properly abstracted. [01:17:34] It was architected just right. [01:17:36] Systems biosis. [01:17:37] Open chip sets. [01:17:38] The operating system. [01:17:39] With. [01:17:40] Drivers. [01:17:41] Drivers. [01:17:42] Drivers. [01:17:43] That could be connected. [01:17:44] And installed at runtime. [01:17:45] And. [01:17:46] An abstraction layer. [01:17:47] With. [01:17:48] A multimedia. [01:17:49] API. [01:17:50] That was. [01:17:51] That opened up the PC. [01:17:52] To what we all know today. [01:17:53] Each one of these elements. [01:17:53] These elements. [01:17:54] This was properly abstracted. [01:17:55] It was architected. [01:17:56] Just right. [01:17:57] Systems biosis. [01:17:58] Open chip sets. [01:17:59] The operating system. [01:18:00] With. [01:18:01] Drivers. [01:18:02] Drivers. [01:18:03] Drivers. [01:18:04] That could be connected. [01:18:05] And installed at runtime. [01:18:06] And. [01:18:07] An abstraction layer. [01:18:07] One of these elements. [01:18:08] Were essential. [01:18:09] In making the PC. [01:18:10] So popular. [01:18:11] Forty years later. [01:18:14] Microsoft and NVIDIA. [01:18:17] Are going to reinvent the PC. [01:18:19] This is. [01:18:21] Going to be the new PC. [01:18:23] Now tomorrow night. [01:18:24] Tomorrow night. [01:18:25] I think it's tomorrow night. [01:18:26] Our time. [01:18:27] But. [01:18:28] I'm going to be with Satya. [01:18:29] We're going to talk a lot more. [01:18:31] About the work that we're doing together. [01:18:33] Microsoft and NVIDIA. [01:18:34] Over the last three years. [01:18:36] It took this long. [01:18:38] To completely reinvent. [01:18:39] How the PC is going to work. [01:18:41] So that we could be ready for this moment. [01:18:43] As I mentioned earlier. [01:18:46] That compute pattern called the agent. [01:18:49] It's going to run in AI clouds. [01:18:51] It's going to run inside. [01:18:53] Enterprises. [01:18:54] It is also going to run on your PC. [01:18:57] What's going to happen to that PC. [01:18:58] When it has an autonomous agent. [01:19:00] An agent that's helping you. [01:19:01] That understands you. [01:19:02] You could talk to it. [01:19:03] It could look at you. [01:19:04] You could. [01:19:05] Ask it to read files. [01:19:06] Go help you. [01:19:07] Do some research. [01:19:08] It could do a lot more. [01:19:09] That I'll show you. [01:19:10] But the new operating system. [01:19:12] Is of course. [01:19:13] The old operating system. [01:19:14] Plus. [01:19:15] Large language models. [01:19:16] Large language models. [01:19:17] In a lot of ways. [01:19:18] Is the modern version. [01:19:19] Of DirectX. [01:19:20] It has. [01:19:21] Of course. [01:19:22] Input and output. [01:19:23] Understands prompts. [01:19:24] It understands computer vision. [01:19:25] It can generate video. [01:19:26] It can generate sounds. [01:19:27] It is the modern extension. [01:19:29] The intelligence extension. [01:19:30] Of the PC. [01:19:31] Of a computer. [01:19:32] On top of that. [01:19:33] The application. [01:19:34] The application. [01:19:35] The application. [01:19:36] Is of course. [01:19:37] Is of course. [01:19:38] The old operating system. [01:19:39] Plus. [01:19:40] Large language models. [01:19:41] Large language models. [01:19:42] In a lot of ways. [01:19:43] Is the modern version. [01:19:44] Of direct X. [01:19:45] It has. [01:19:46] Of course. [01:19:47] Input and output. [01:19:48] On top of that. [01:19:49] The application. [01:19:50] As I mentioned before. [01:19:51] Is going to be. [01:19:52] Replaced. [01:19:53] By now. [01:19:54] An agentic. [01:19:55] Runtime. [01:19:56] And that. [01:19:57] Is the modern application. [01:19:58] An agent. [01:19:59] Let's now. [01:20:00] Take a look. [01:20:01] At what it can do. [01:20:04] It started. [01:20:05] With a spark. [01:20:06] An idea. [01:20:08] To reimagine. [01:20:09] The PC. [01:20:10] For the first time. [01:20:11] In 40 years. [01:20:12] For the age. [01:20:13] Of AI. [01:20:14] What becomes. [01:20:16] Of our personal computer. [01:20:17] In a world. [01:20:18] Of agents. [01:20:19] Agents. [01:20:20] Running natively. [01:20:21] Connected to models. [01:20:22] Local. [01:20:23] Or in the cloud. [01:20:24] Our personal AI. [01:20:25] Sandboxed for security. [01:20:26] Running continuously. [01:20:27] Getting work done. [01:20:28] The chips. [01:20:29] And the OS. [01:20:30] Must evolve. [01:20:31] Introducing. [01:20:32] RTX Spark. [01:20:33] Everything we've learned. [01:20:34] Over 33 years. [01:20:35] Distilled into one chip. [01:20:36] Blackwell RTX GPU. [01:20:37] With 6,144 CUDA cores. [01:20:39] One petaflop. [01:20:40] Of AI performance. [01:20:41] A custom 20 core grace. [01:20:43] CPU. [01:20:44] Built in partnership. [01:20:45] With MediaTek. [01:20:46] Fused. [01:20:47] By MVLink. [01:20:48] 128 gigabytes. [01:20:48] Of unified memory. [01:20:49] TSMC 3 nanometer process. [01:20:50] 70 billion transistors. [01:20:51] And in close collaboration. [01:20:52] With Microsoft. [01:20:53] A Windows platform. [01:20:54] For agents. [01:20:55] We're reinventing. [01:20:56] The personal computer. [01:20:57] For creating. [01:20:58] built in partnership with MediaTek, fused by MVLink, 128 gigabytes of unified memory, [01:21:09] TSMC 3 nanometer process, 70 billion transistors, and in close collaboration with Microsoft, [01:21:18] a Windows platform for agents. We're reinventing the personal computer. [01:21:25] For creating. For gaming. For agents. [01:21:33] This is the dawn of a new personal computing revolution. [01:21:37] And it starts with NVIDIA RTX SMART. [01:21:51] Here it is. [01:21:58] Of course, I got to show you the most beautiful part, which is video games. [01:22:03] It is also the closest to our heart. This is Forza. This is 007, by the way. [01:22:11] The new 007 game. I'm looking forward to playing it. [01:22:14] I look a little bit like him. [01:22:16] Ladies and gentlemen, NVIDIA's RTX SPARK laptops. [01:22:21] This is the most amazing chip the world's ever built. [01:22:27] This is the N1X that we built in partnership with MediaTek. [01:22:31] I think I saw Rick earlier. This is the N1X. This is the N1X. This is a beautiful chip. [01:22:44] This is a chip that frankly would take 33 years to build. And the reason for that is because 100% of NVIDIA software stack runs here. [01:23:02] If you want to run digital biology, no problem. If you want to do seismic processing, no problem. You want astrophysics, no problem. [01:23:11] Everything associated with CUDA, all the physics, all the biology, all the genomics. All the AI, no problem. All the computer graphics, no problem. [01:23:23] Every single application NVIDIA has ever created and every single application that Windows has ever run. [01:23:32] Microsoft and NVIDIA meticulously optimized everything so that this computer literally runs all the AI. [01:23:39] The world has ever created plus it now runs agents. An incredible computer. I'm so proud of it. [01:23:57] Okay. Now, I want you to keep that in mind in the next video. I just, I'm going to show you. [01:24:04] Just imagine everything here is going to run on your PC. [01:24:07] Now that computer could have a local Nemo Tron three ultra model or Nemo Tron three supermodel, or it could have a cloud code or codex or some other model in the cloud or something on the network. [01:24:23] And it's going to, it's going to work and do something amazing. Let's play. [01:24:29] Now, an agent running locally on RTX Spark can help me design a house using the tools on my laptop with an open shell sandbox running the Hermes harness connected to Claude Sonnet in the cloud. [01:24:53] I select the site. I select the site, share my concept sketches and mood board of styles to inspire my design and the prompt, a text description of the requirements and the design intent. [01:25:06] My agent goes to work using the tools on my laptop, it opens Rhino and starts modeling the site, shaping terrain, setbacks and the building envelope. [01:25:20] Then it proposes building forms optimized for cost, comfort and quality. [01:25:25] With the form defined, my agent generates the interior layout, walls, circulation, rooms begin to take shape. [01:25:34] I jump in whenever I want to adjust to change. [01:25:38] Doors, windows and structural elements are placed automatically. [01:25:45] My agent detects its own mistakes and fixes them. [01:25:50] When I approve, the agent exports the model from Rhino into Blender. [01:25:57] Materials and object properties transfer with the design context intact. [01:26:02] I fine tune the materials, get the look just right. [01:26:07] Then I pick the shots. [01:26:09] Blender renders the house. [01:26:11] My agent using generative AI with the Flux 2 model makes them photo real. [01:26:15] Multiple viewpoints, lighting conditions. [01:26:18] What was once a complex workflow is now guided and simplified by my agent. [01:26:24] Working with on RTX Spark. [01:26:27] Design at the speed of imagination. [01:26:30] PC and the world of agents. [01:26:39] The developers are so excited about it. [01:26:41] This is an incredible computer. [01:26:43] All of the acceleration, all the software capabilities associated with it. [01:26:47] Working with every developer to make it incredible for all of you. [01:26:51] The next one, Adobe. [01:26:54] Incredible tool suite, of course, used by tens of millions of people around the world. [01:27:00] They have re-engineered the architecture, the core of Adobe Photoshop and Premiere. [01:27:06] And they will release it for RTX Spark. [01:27:08] It is twice as fast. [01:27:09] It's already fast. [01:27:10] Now it's going to be twice as fast. [01:27:12] And it's also designed to be agent friendly. [01:27:15] With its MCP server, it can now interact with agents on your laptop. [01:27:21] The number of customers, the number of partners that are so excited to bring RTX Spark to the market is just incredible. [01:27:30] You know, this is the first across the lineup of PC re-invention for 40 years. [01:27:37] And I'm just so happy that all of you and the ecosystem around the world has joined us. [01:27:43] This is basically everybody. [01:27:45] Everybody will support RTX Spark and will be building incredibly smart and powerful and beautiful laptops with all of us. [01:27:53] With all of us, thank you very much. [01:28:01] But that's not all. [01:28:03] That's not all. [01:28:05] RTX Spark is a re-invention of laptop. [01:28:09] But in fact, Microsoft NVIDIA is re-inventing all of PC. [01:28:14] And today we're announcing a whole new line. [01:28:18] Three revolutionary Windows machines covering desktop, laptop, and workstations. [01:28:26] All 100% Windows compatible, 100% CUDA, 100% NVIDIA AI Tensor Core. [01:28:34] Everything that runs, that you see that runs on NVIDIA and all these different platforms around the world, runs here. [01:28:41] This is the first completely re-engineered, re-invented line of PCs that has happened in 40 years. [01:28:50] Now, what's really amazing is this. [01:28:52] So, this is the RTX Spark laptop. [01:28:55] This is the desktop. [01:28:59] So, this one's from MSI. [01:29:01] Joseph, this one's yours. [01:29:03] Okay? [01:29:04] Look how beautiful it is. [01:29:05] This agent could run 24/7, meter free. [01:29:10] And you could download your agent. [01:29:12] You could raise your lobster. [01:29:33] Dryer, your water cooler, your water heater, your everything. [01:29:37] Whatever you want. [01:29:38] Your security system all connected to this. [01:29:40] And this becomes your personal AI. [01:29:43] Your personal AI agent. [01:29:45] And it gets smarter and smarter and smarter over time. [01:29:48] Because today we have NemoTron 3 Ultra. [01:29:51] Tomorrow we have NemoTron 4. [01:29:53] And then NemoTron 5. [01:29:54] NemoTron 6. [01:29:55] And we just keep getting us smarter and smarter and smarter. [01:29:57] And meanwhile, this is sitting at home helping you do things. [01:30:01] If you want to book a travel, no problem. [01:30:04] And if you want an incredible system, this is a DGX station for Windows. [01:30:16] Compatible with Windows. [01:30:17] It wants everything in Windows. [01:30:19] And it has 768 gigabytes of memory. [01:30:25] And so, you could run a trillion parameter model. [01:30:28] This is unbelievable. [01:30:30] 20 petaflops. [01:30:33] 8 terabytes per second of memory bandwidth. [01:30:36] And this sits by your desk. [01:30:39] You basically, if you're a developer of large language models, you're a developer of agents. [01:30:45] Having this sit by your desk gives you all the compute you need. [01:30:49] And then when you deploy it, you put it into the cloud. [01:30:52] Now, there's something that, if you look at this and think about this, something is happening here. [01:30:58] Remember, 15, 20 years ago, we used to have an idea called a phone. [01:31:05] Today, we have an idea called a PC. [01:31:10] Today, when you think about your phone. [01:31:13] Today, when you think about your phone. [01:31:15] The one thing you don't do with it is make phone calls. [01:31:19] You do just about everything else. [01:31:21] You do just about everything else. [01:31:23] And so, that phone means something very different to you than a phone of the past. [01:31:28] Now, I am certain what's going to happen here is that the PC, 10 years from now, and the PC that you think about today, a tool, whether you launch applications. [01:31:41] Click and type. [01:31:43] Click and type. [01:31:44] And this PC is going to be completely different. [01:31:48] Here's my theory. [01:31:49] I can totally imagine, just as every house today has a home theater, where many houses have home theaters, big TVs, lawn mowers, dishwashers. [01:32:04] I can totally imagine that someday, there's actually an AI supercomputer in your house. [01:32:10] And it's running all of your agents. [01:32:12] It's running all of your assistants. [01:32:14] And they're doing all kinds of things for you all the time. [01:32:18] And you have to have it in your house, just like you have a home theater in your house. [01:32:22] You have stereos in your house. [01:32:23] You have game consoles in your house. [01:32:26] You want to assist AI agent computers running in your house. [01:32:30] And these, in time, becomes a lot more like R2D2 to you. [01:32:37] It becomes more like C3PO to you. [01:32:41] Then it feels like a PC to you. [01:32:44] There is no question this reinvention of the computer is as big of a deal as the reinvention of the phone into what we now know as the smartphone. [01:32:54] And so this is the beginning of that journey. [01:32:56] This is the beginning of a new line. [01:32:59] And so we have a roadmap for this. [01:33:01] This is a brand new product family for us. [01:33:04] Every single generation of architecture, we will have a desktop, a laptop, a workstation, and then a desktop, a laptop and workstation. [01:33:15] And the thing that I am just incredibly pleased, incredibly honored is that 100% of the world's PC industry has joined us to reinvent the PC, a new line, a new beginning. [01:33:29] Thank you. [01:33:43] As you know, agentic AI is just a digital robot. [01:33:50] It understands, it reasons, it plans, and it acts and use tools. [01:33:57] Agentic AI is going to run across all of these computers. [01:34:02] And you've seen me talk about each and every one of these over time. [01:34:05] We're working on human or robotics computers, robotics computers of all kinds. [01:34:10] We're working on self-driving car computers. [01:34:12] We're working on satellites. [01:34:15] And you have g-force, which has tensor cores. [01:34:17] I just talked about a whole new line of PCs. [01:34:20] Agriculture equipment, manufacturing equipment, heavy industry equipment will all be agentic. [01:34:27] You'll even have a little agentic helper for yourself. [01:34:32] Even your base stations, the radio stations of the future, are going to be agentic. [01:34:37] Understanding traffic and thinking about how to coordinate with the other base stations so that you could use as little energy as possible, increase the utilization, the efficiency of the spectral efficiency. [01:34:50] And so, everything will run agents. [01:34:54] Today, NVIDIA is largely in the center. [01:34:58] But I am pretty certain that there will be tens of billions, hundreds of billions over time of agentic systems, agentic computers that are going to be running around the world. [01:35:10] The biggest problem is data. [01:35:13] In the case of language models, all the English and all the language that we have on the internet that we trained on was from the perspective of data. [01:35:20] It is from the perspective of us. [01:35:22] We wrote it and we're reading it. [01:35:24] However, in order to create data for AI, robotics, it has to be in the perception, the perspective of the robot. [01:35:33] And most of the world's video data is from a third person, not first person. [01:35:39] And so, agentic systems, robotic systems, physical AI, the data is the hardest problem. [01:35:47] You've seen us move up this ladder. [01:35:50] We started with teleoperations, which is basically human demonstration. [01:35:54] This is no different than the big breakthrough of reinforcement learning human feedback. [01:36:00] This, then we use simulation. [01:36:02] This is where Omniverse comes in. [01:36:03] This is no different than reinforcement learning, verifiable rewards. [01:36:09] Okay? [01:36:09] And so, we use these systems to bootstrap the AI model, the physical AI model. [01:36:17] Eventually, we're able to learn from third person, reprojecting it into first person. [01:36:24] And now, eventually, through bootstrapping, we have a world foundation model that can understand the physical world from any perspective you want. [01:36:35] Third person, first person, outside-in, inside-out, doesn't matter. [01:36:40] Third person, outside-in, inside-out, doesn't matter. [01:36:45] Third person, outside-in, inside-out, doesn't matter. [01:36:46] Third person, outside-out, doesn't matter. [01:36:47] Third person, outside-out, doesn't matter. [01:36:48] Third person, outside-out, doesn't matter. [01:36:49] Third person, outside-out, doesn't matter. [01:36:50] Third person, outside-out, doesn't matter. [01:36:51] Third person, outside-out, doesn't matter. [01:36:52] Third person, outside-out, doesn't matter. [01:36:53] Third person, outside-out, doesn't matter. [01:36:54] Third person, outside-out, doesn't matter. [01:36:55] Third person, outside-out, doesn't matter. [01:36:56] Third person, outside-out, doesn't matter. [01:36:57] Third person, outside-out, doesn't matter. [01:36:58] Third person, outside-out, doesn't matter. [01:36:59] Third person, outside-out, doesn't matter. [01:37:00] Third person, outside-out, doesn't matter. [01:37:01] Third person, outside-out, doesn't matter. [01:37:02] Third person, outside-out, doesn't matter. [01:37:03] Third person, outside-out, doesn't matter. [01:37:04] Third person, outside-out, doesn't matter. [01:37:05] Third person, outside-out, doesn't matter. [01:37:06] Third person, outside-out, doesn't matter. [01:37:07] Third person, outside-out, doesn't matter. [01:37:08] Third person, outside-out, doesn't matter. [01:37:09] Third person, outside-out, doesn't matter. [01:37:10] Third person, outside-out, doesn't matter. [01:37:11] Third person, outside-out, doesn't matter. [01:37:12] Third person, outside-out, doesn't matter. [01:37:14] Third person, outside-out, doesn't matter. [01:37:15] Third person, outside-out, doesn't matter. [01:37:17] Third person, outside-out, doesn't matter. [01:37:18] Third person, outside-out, doesn't matter. [01:37:19] Third person, outside-out, doesn't matter. [01:37:20] Third person, outside-out, doesn't matter. [01:37:21] Third person, outside-out, doesn't matter. [01:37:22] Third person, outside-out, doesn't matter. [01:37:23] Third person, outside-out, doesn't matter. [01:37:24] Third person, outside-out, doesn't matter. [01:37:25] Third person, outside-out, doesn't matter. [01:37:26] Third person, outside-out, doesn't matter. [01:37:27] Third person, outside-out, doesn't matter. [01:37:28] Third person, outside-out, doesn't matter. [01:37:29] Third person, outside-out, doesn't matter. [01:37:30] Third person, outside-out, doesn't matter. [01:37:31] Third person, outside-out, doesn't matter. [01:37:32] Third person, outside-out, doesn't matter. [01:37:33] Third person, outside-out, doesn't matter. [01:37:34] Third person, outside-out, doesn't matter. [01:37:35] Third person, outside-out, doesn't matter. [01:37:36] Third person, outside-out, doesn't matter. [01:37:37] Third person, outside-out, doesn't matter. [01:37:38] Third person, outside-out, doesn't matter. [01:37:39] Third person, outside-out, doesn't matter. [01:37:40] Third person, outside-out, doesn't matter. [01:37:41] Third person, outside-out, doesn't matter. [01:37:42] Third person, outside-out, doesn't matter. [01:37:43] Third person, outside-out, doesn't matter. [01:37:44] Third person, outside-out, doesn't matter. [01:37:45] Third person, outside-out, doesn't matter. [01:37:46] Third person, outside-out, doesn't matter. [01:37:47] Third person, outside-out, doesn't matter. [01:37:48] Third person, outside-out, needs data. [01:37:49] But real-world data is impossible to scale. [01:37:52] For physical AI, compute is data. [01:37:56] This is Cosmos, an open frontier omnimodel for physical AI, [01:38:01] built on a new mixture of Transformers architecture. [01:38:04] Pixels, action, sound, and language [01:38:07] flow into the autoregressive Transformer, [01:38:09] which reasons, plans, and instructs the Diffusion Transformer, [01:38:14] which generates what comes next. [01:38:16] Developers post-train Cosmos across embodiments and use cases. [01:38:21] As a VLM, Cosmos watches the physical world, [01:38:25] understands what's happening, [01:38:27] describing scenes, and flagging what matters. [01:38:31] As a world model, Cosmos generates physics-accurate synthetic video [01:38:36] from an image, text, or video. [01:38:40] As a simulator, Cosmos closes the loop [01:38:43] for policy training and evaluation. [01:38:45] And as the foundation of NVIDIA Omnidreams, [01:38:48] an action-conditioned world model, [01:38:50] Cosmos predicts the future, frame by frame. [01:38:53] Post-train Cosmos, and it becomes a world action model. [01:38:58] Perceiving, reasoning, planning, generating actions. [01:39:03] For robots of every kind, for everything that moves. [01:39:11] A new kind of data, a new kind of teacher, generated by compute. [01:39:19] Cosmos, the foundation for developers of the age of physical AI. [01:39:25] It takes data plus compute, gives you AI. [01:39:41] Now that we have AI, compute is data. [01:39:46] And so, use Cosmos 3, train a whole bunch of AI models. [01:39:50] Cosmos is such an incredible open model system. [01:39:52] It's exactly the same as Nemo Tron. [01:39:54] We open the model. [01:39:55] We open the data. [01:39:56] And we even open how we trained it, so that you could enhance it for yourself. [01:40:01] And turn Cosmos into your proprietary model. [01:40:04] We have such incredible partners working with us in so many different industries. [01:40:08] Now, the model itself is that most, of course, the most understandable part of the AI stack. [01:40:15] But the AI stack is very complicated. [01:40:17] It has generators, the model, simulators, and the runtime. [01:40:24] Just as it is for agentic systems, these cars are essentially a physical AI agentic robot. [01:40:33] That is an autonomous vehicle, has also this complicated stack. [01:40:38] Today, we're announcing Alpamio 2, an open model for self-driving cars. [01:40:45] We're working with car companies across the world. [01:40:49] If you look at these brands that have signed up for the NVIDIA Hyperion, that are building NVIDIA Hyperion cars. [01:40:55] This represents about 80% of the world's cars. [01:41:01] Manufacturers represent 80% of the world's cars. [01:41:04] We are going to have a whole lot of NVIDIA Hyperion systems that are able to run Alpamio or anybody else's AV stack. [01:41:14] We are connected into mobility services. [01:41:17] Approximately 97% of the world's mobility services are connecting with us so that when we deploy Alpamio on the Hyperion runtime [01:41:27] with the Halos operating system, we will be able to connect to all of these services across the world. [01:41:32] Let's take a look at this. [01:41:33] Hey Mercedes, let's go to my favourite sandwich shop. [01:41:41] Routing to your destination. [01:41:44] Lane is clear. [01:41:45] Pulling out to start drive. [01:41:47] Nudge left due to the stationary lead vehicle ahead blocking our lane. [01:41:51] Slow down to stop at the stop sign controlling the intersection. [01:41:54] Stop to yield to the pedestrian since the person is in our lane. [01:41:58] Nudge left due to the lead vehicle. [01:41:59] Nudge right due to the truck blocking on the left side of our lane. [01:42:01] Nudge left due to the truck blocking on the left side of our lane. [01:42:04] Nudge left due to the truck blocking the left side of our lane. [01:42:06] Nudge left due to the truck blocking on the left side of our lane. [01:42:08] Nudge left due to the truck blocking the left side of our lane. [01:42:10] Nudge left due to the truck blocking the left side of our lane. [01:42:12] Nudge left due to the truck blocking the left side of our lane. [01:42:14] Nudge left due to the truck blocking the left side of our lane. [01:42:16] Nudge left due to the truck blocking the right side of our lane. [01:42:18] Nudge left due to the truck blocking the left side of our lane. [01:42:21] Nudge left due to the truck blocking the right side of our lane. [01:42:23] Nudge left due to the truck blocking the right side of our lane. [01:42:24] Nudge left due to the truck blocking the right side of our lane. [01:42:25] Nudge left due to the truck blocking the right side of our lane. [01:42:26] Nudge left due to the truck blocking the right side of our lane. [01:42:27] Nudge left due to the truck blocking the right side of our lane. [01:42:28] Nudge left due to the truck blocking the right side of our lane. [01:42:29] Nudge left due to the truck blocking the right side of our lane. [01:42:30] Nudge left due to the truck blocking the right side of our lane. [01:42:31] Nudge left due to the truck blocking the right side of our lane. [01:42:32] Nudge left due to the truck blocking the right side of our lane. [01:42:33] Your destination is on the right. [01:42:34] Nudge left due to the truck blocking the right side of our lane. [01:42:39] Nudge left due to the truck blocking the right side of our lane. [01:42:41] Nudge left due to the truck blocking the right side of our lane. [01:42:42] Nudge left due to the truck blocking the right side of our lane. [01:42:43] Nudge left due to the truck blocking the right side of our lane. [01:42:44] Nudge left due to the truck blocking the right side of our lane. [01:42:45] Nudge left due to the truck blocking the right side of our lane. [01:42:46] Nudge left due to the truck blocking the right side of our lane. [01:42:47] Nudge left due to the truck blocking the right side of our lane. [01:42:48] Nudge left due to the truck blocking the right side of our lane. [01:42:49] Nudge left due to the truck blocking the right side of our lane. [01:42:50] Nudge left due to the truck blocking the right side of our lane. [01:42:51] Nudge left due to the truck blocking the right side of our lane. [01:42:54] Nudge left due to the truck blocking the right side of our lane. [01:42:58] Nudge left due to the truck blocking the right side of our lane. [01:43:00] Nudge left due to the truck blocking the right side of our lane. [01:43:02] Nudge left due to the truck blocking the right side of our lane. [01:43:04] Nudge left due to the truck blocking the right side of our lane. [01:43:06] Nudge left due to the truck blocking the right side of our lane. [01:43:08] Nudge left due to the truck blocking the right side of our lane. [01:43:10] Nudge left due to the truck blocking the right side of our lane. [01:43:12] Nudge left due to the truck blocking the right side of our lane. [01:43:14] Nudge left due to the truck blocking the right side of our lane. [01:43:16] Nudge left due to the truck blocking the right side of our lane. [01:43:20] Nudge left due to the truck blocking the right side of our lane. [01:43:24] Nudge left due to the truck blocking the right side of our lane. [01:43:26] Nudge left due to the truck blocking the right side of our lane. [01:43:28] Nudge left due to the truck blocking the right side of our lane. [01:43:30] Nudge left due to the truck blocking the right side of our lane. [01:43:32] Nudge left due to the truck blocking the right side of our lane. [01:43:34] Nudge left due to the truck blocking the right side of our lane. [01:43:36] Nudge left due to the truck blocking the right side of our lane. [01:43:38] Nudge left due to the truck blocking the right side of our lane. [01:43:40] Nudge left due to the truck blocking the right side of our lane. [01:43:42] Nudge left due to the truck blocking the right side of our lane. [01:43:44] Nudge left due to the truck blocking the right side of our lane. [01:43:47] Nudge left due to the truck blocking the right side of our lane. [01:43:49] Nudge left due to the truck blocking the right side of our lane. [01:43:51] Nudge left due to the truck blocking the right side of our lane. [01:43:53] Nudge left due to the truck blocking the right side of our lane. [01:43:55] Nudge left due to the truck blocking the right side of our lane. [01:43:57] Nudge left due to the truck blocking the right side of our lane. [01:43:59] Nudge left due to the truck blocking the right side of our lane. [01:44:03] Nudge left due to the truck blocking the right side of our lane. [01:44:05] Nudge left due to the truck blocking the right side of our lane. [01:44:07] Nudge left due to the truck blocking the right side of our lane. [01:44:09] Nudge left due to the truck blocking the right side of our lane. [01:44:11] Nudge left due to the truck blocking the right side of our lane. [01:44:13] Nudge left due to the truck blocking the right side of our lane. [01:44:15] Nudge left due to the truck blocking the right side of our lane. [01:44:17] Nudge left due to the truck blocking the right side of our lane. [01:44:19] Nudge left due to the truck blocking the right side of our lane. [01:44:21] Nudge left due to the truck blocking the right side of our lane. [01:44:23] Nudge left due to the truck blocking the right side of our lane. [01:44:25] Nudge left due to the truck blocking the right side of our lane. [01:44:27] Nudge left due to the truck blocking the right side of our lane. [01:44:29] Nudge left due to the truck blocking the right side of our lane. [01:44:31] Nudge left due to the truck blocking the right side of our lane. [01:44:33] Nudge left due to the truck blocking the right side of our lane. [01:44:35] Nudge left due to the truck blocking the right side of our lane. [01:44:37] Nudge left due to the truck blocking the right side of our lane. [01:44:41] Nudge left due to the truck blocking the right side of our lane. [01:44:44] Nudge left due to the truck blocking the right side of our lane. [01:44:46] Nudge left due to the truck blocking the right side of our lane. [01:44:48] Nudge left due to the truck blocking the right side of our lane. [01:44:50] Nudge left due to the truck blocking the right side of our lane. [01:44:52] Nudge left due to the truck blocking the right side of our lane. [01:44:54] Nudge left due to the truck blocking the right side of our lane. [01:44:56] Nudge left due to the truck blocking the right side of our lane. [01:44:58] Nudge left due to the truck blocking the right side of our lane. [01:45:00] Nudge left due to the truck blocking the right side of our lane. [01:45:02] Nudge left due to the truck blocking the right side of our lane. [01:45:04] Nudge left due to the truck blocking the right side of our lane. [01:45:07] Nudge left due to the truck blocking the right side of our lane. [01:45:09] Nudge left due to the truck blocking the right side of our lane. [01:45:11] Nudge left due to the truck blocking the right side of our lane. [01:45:13] Nudge left due to the truck blocking the right side of our lane. [01:45:15] Nudge left due to the truck blocking the right side of our lane. [01:45:17] Nudge left due to the truck blocking the right side of our lane. [01:45:19] Nudge left due to the truck blocking the right side of our lane. [01:45:21] Nudge left due to the truck blocking the right side of our lane. [01:45:23] Nudge left due to the truck blocking the right side of our lane. [01:45:27] Nudge left due to the truck blocking the right side of our lane. [01:45:29] Nudge left due to the truck blocking the right side of our lane. [01:45:31] Nudge left due to the truck blocking the right side of our lane. [01:45:34] Nudge left due to the truck blocking the right side of our lane. [01:45:36] Nudge left due to the truck blocking the right side of our lane. [01:45:38] Nudge left due to the truck blocking the right side of our lane. [01:45:42] Nudge left due to the truck blocking the right side of our lane. [01:45:44] Nudge left due to the truck blocking the right side of our lane. [01:45:46] Nudge left due to the truck blocking the right side of our lane. [01:45:48] Nudge left due to the truck blocking the right side of our lane. [01:45:50] Nudge left due to the truck blocking the right side of our lane. [01:45:52] Nudge left due to the truck blocking the right side of our lane. [01:45:54] Nudge left due to the truck blocking the right side of our lane. [01:45:56] Nudge left due to the truck blocking the right side of our lane. [01:45:58] Nudge left due to the truck blocking the right side of our lane. [01:46:00] Nudge left due to the truck blocking the right side of our lane. [01:46:02] Nudge left due to the truck blocking the right side of our lane. [01:46:04] Nudge left due to the truck blocking the right side of our lane. [01:46:08] Nudge left due to the truck blocking the right side of our lane. [01:46:10] Nudge left due to the truck blocking the right side of our lane. [01:46:12] Nudge left due to the truck blocking the right side of our lane. [01:46:14] Nudge left due to the truck blocking the right side of our lane. [01:46:16] Nudge left due to the truck blocking the right side of our lane. [01:46:18] Nudge left due to the truck blocking the right side of our lane. [01:46:20] Nudge left due to the truck blocking the right side of our lane. [01:46:22] Nudge left due to the truck blocking the right side of our lane. [01:46:24] Nudge left due to the truck blocking the right side of our lane. [01:46:26] Nudge left due to the truck blocking the right side of our lane. [01:46:28] Nudge left due to the truck blocking the right side of our lane. [01:46:30] Nudge left due to the truck blocking the right side of our lane. [01:46:32] Nudge left due to the truck blocking the right side of our lane. [01:46:34] Nudge left due to the truck blocking the right side of our lane. [01:46:36] Nudge left due to the truck blocking the right side of our lane. [01:46:44] Nudge left due to the truck blocking the right side of our lane. [01:46:46] Nudge left due to the truck blocking the right side of our lane. [01:46:48] Nudge left due to the truck blocking the right side of our lane. [01:46:50] Nudge left due to the truck blocking the right side of our lane. [01:46:52] Nudge left due to the truck blocking the right side of our lane. [01:46:54] Nudge left due to the truck blocking the right side of our lane. [01:46:56] Nudge left due to the truck blocking the right side of our lane. [01:46:58] Nudge left due to the truck blocking the right side of our lane. [01:47:00] Nudge left due to the truck blocking the right side of our lane. [01:47:02] Nudge left due to the truck blocking the right side of our lane. [01:47:04] Nudge left due to the truck blocking the right side of our lane. [01:47:06] Nudge left due to the truck blocking the right side of our lane. [01:47:08] Nudge left due to the truck blocking the right side of our lane. [01:47:09] Nudge left due to the truck blocking the right side of our lane. [01:47:10] Nudge left due to the truck blocking the right side of our lane. [01:47:11] Nudge left due to the truck blocking the right side of our lane. [01:47:12] Nudge left due to the truck blocking the right side of our lane. [01:47:13] Nudge left due to the truck blocking the right side of our lane. [01:47:14] Nudge left due to the truck blocking the right side of our lane. [01:47:15] Nudge left due to the truck blocking the right side of our lane. [01:47:16] Nudge left due to the truck blocking the right side of our lane. [01:47:17] Nudge left due to the truck blocking the right side of our lane. [01:47:18] Nudge left due to the truck blocking the right side of our lane. [01:47:19] Nudge left due to the truck blocking the right side of our lane. [01:47:20] Nudge left due to the truck blocking the right side of our lane. [01:47:21] Nudge left due to the truck blocking the right side of our lane. [01:47:22] Nudge left due to the truck blocking the right side of our lane. [01:47:25] Nudge left due to the truck blocking the right side of our lane. [01:47:27] Nudge left due to the truck blocking the right side of our lane. [01:47:28] Nudge left due to the truck blocking the right side of our lane. [01:47:29] Nudge left due to the truck blocking the right side of our lane. [01:47:30] Nudge left due to the truck blocking the right side of our lane. [01:47:31] Nudge left due to the truck blocking the right side of our lane. [01:47:32] Nudge left due to the truck blocking the right side of our lane. [01:47:33] Nudge left due to the truck blocking the right side of our lane. [01:47:34] Nudge left due to the truck blocking the right side of our lane. [01:47:35] Nudge left due to the truck blocking the right side of our lane. [01:47:36] Nudge left due to the truck blocking the right side of our lane. [01:47:37] Nudge left due to the truck blocking the right side of our lane. [01:47:40] Nudge left due to the truck blocking the right side of our lane. [01:47:42] Nudge left due to the truck blocking the right side of our lane. [01:47:43] Nudge left due to the truck blocking the right side of our lane. [01:47:44] Nudge left due to the truck blocking the right side of our lane. [01:47:45] Nudge left due to the truck blocking the right side of our lane. [01:47:46] Nudge left due to the truck blocking the right side of our lane. [01:47:47] Nudge left due to the truck blocking the right side of our lane. [01:47:48] Nudge left due to the truck blocking the right side of our lane. [01:47:49] Nudge left due to the truck blocking the right side of our lane. [01:47:50] Nudge left due to the truck blocking the right side of our lane. [01:47:51] Nudge left due to the truck blocking the right side of our lane. [01:47:52] Nudge left due to the truck blocking the right side of our lane. [01:47:55] Nudge left due to the truck blocking the right side of our lane. [01:47:57] Nudge left due to the truck blocking the right side of our lane. [01:47:58] Nudge left due to the truck blocking the right side of our lane. [01:47:59] Nudge left due to the truck blocking the right side of our lane. [01:48:00] Nudge left due to the truck blocking the right side of our lane. [01:48:01] Nudge left due to the truck blocking the right side of our lane. [01:48:02] Nudge left due to the truck blocking the right side of our lane. [01:48:03] Nudge left due to the truck blocking the right side of our lane. [01:48:04] Nudge left due to the truck blocking the right side of our lane. [01:48:05] Nudge left due to the truck blocking the right side of our lane. [01:48:06] Nudge left due to the truck blocking the right side of our lane. [01:48:07] Nudge left due to the truck blocking the right side of our lane. [01:48:10] Nudge left due to the truck blocking the right side of our lane. [01:48:12] Nudge left due to the truck blocking the right side of our lane. [01:48:13] Nudge left due to the truck blocking the right side of our lane. [01:48:14] Nudge left due to the truck blocking the right side of our lane. [01:48:15] Nudge left due to the truck blocking the right side of our lane. [01:48:16] Nudge left due to the truck blocking the right side of our lane. [01:48:17] Nudge left due to the truck blocking the right side of our lane. [01:48:18] Nudge left due to the truck blocking the right side of our lane. [01:48:19] Nudge left due to the truck blocking the right side of our lane. [01:48:20] Nudge left due to the truck blocking the right side of our lane. [01:48:21] Nudge left due to the truck blocking the right side of our lane. [01:48:22] Nudge left due to the truck blocking the right side of our lane. [01:48:25] Nudge left due to the truck blocking the right side of our lane. [01:48:27] Nudge left due to the truck blocking the right side of our lane. [01:48:28] Nudge left due to the truck blocking the right side of our lane. [01:48:29] Nudge left due to the truck blocking the right side of our lane. [01:48:30] Nudge left due to the truck blocking the right side of our lane. [01:48:31] Nudge left due to the truck blocking the right side of our lane. [01:48:32] Nudge left due to the truck blocking the right side of our lane. [01:48:33] Nudge left due to the truck blocking the right side of our lane. [01:48:34] Nudge left due to the truck blocking the right side of our lane. [01:48:35] Nudge left due to the truck blocking the right side of our lane. [01:48:36] Nudge left due to the truck blocking the right side of our lane. [01:48:37] Nudge left due to the truck blocking the right side of our lane. [01:48:40] Nudge left due to the truck blocking the right side of our lane. [01:48:42] Nudge left due to the truck blocking the right side of our lane. [01:48:43] Nudge left due to the truck blocking the right side of our lane. [01:48:44] Nudge left due to the truck blocking the right side of our lane. [01:48:45] Nudge left due to the truck blocking the right side of our lane. [01:48:46] Nudge left due to the truck blocking the right side of our lane. [01:48:47] Nudge left due to the truck blocking the right side of our lane. [01:48:48] Nudge left due to the truck blocking the right side of our lane. [01:48:49] Nudge left due to the truck blocking the right side of our lane. [01:48:50] Nudge left due to the truck blocking the right side of our lane. [01:48:51] Nudge left due to the truck blocking the right side of our lane. [01:48:52] Nudge left due to the truck blocking the right side of our lane. [01:48:56] Nudge left due to the truck blocking the right side of our lane. [01:48:57] Nudge left due to the truck blocking the right side of our lane. [01:48:58] Nudge left due to the truck blocking the right side of our lane. [01:48:59] Nudge left due to the truck blocking the right side of our lane. [01:49:00] Nudge left due to the truck blocking the right side of our lane. [01:49:01] Nudge left due to the truck blocking the right side of our lane. [01:49:02] Nudge left due to the truck blocking the right side of our lane. [01:49:03] Nudge left due to the truck blocking the right side of our lane. [01:49:04] Nudge left due to the truck blocking the right side of our lane. [01:49:05] Nudge left due to the truck blocking the right side of our lane. [01:49:06] Nudge left due to the truck blocking the right side of our lane. [01:49:07] Nudge left due to the truck blocking the right side of our lane. [01:49:10] Nudge left due to the truck blocking the right side of our lane. [01:49:12] Nudge left due to the truck blocking the right side of our lane. [01:49:13] Nudge left due to the truck blocking the right side of our lane. [01:49:14] Nudge left due to the truck blocking the right side of our lane. [01:49:15] Nudge left due to the truck blocking the right side of our lane. [01:49:16] Nudge left due to the truck blocking the right side of our lane. [01:49:17] Nudge left due to the truck blocking the right side of our lane. [01:49:18] Nudge left due to the truck blocking the right side of our lane. [01:49:19] Nudge left due to the truck blocking the right side of our lane. [01:49:20] Nudge left due to the truck blocking the right side of our lane. [01:49:21] Nudge left due to the truck blocking the right side of our lane. [01:49:22] Nudge left due to the truck blocking the right side of our lane. [01:49:25] Nudge left due to the truck blocking the right side of our lane. [01:49:27] Nudge left due to the truck blocking the right side of our lane. [01:49:28] Nudge left due to the truck blocking the right side of our lane. [01:49:29] Nudge left due to the truck blocking the right side of our lane. [01:49:30] Nudge left due to the truck blocking the right side of our lane. [01:49:31] Nudge left due to the truck blocking the right side of our lane. [01:49:32] Nudge left due to the truck blocking the right side of our lane. [01:49:33] Nudge left due to the truck blocking the right side of our lane. [01:49:34] Nudge left due to the truck blocking the right side of our lane. [01:49:35] Nudge left due to the truck blocking the right side of our lane. [01:49:36] Nudge left due to the truck blocking the right side of our lane. [01:49:37] Nudge left due to the truck blocking the right side of our lane. [01:49:39] Nudge left due to the truck blocking the right side of our lane. [01:49:41] Nudge left due to the truck blocking the right side of our lane. [01:49:42] Nudge left due to the truck blocking the right side of our lane. [01:49:43] Nudge left due to the truck blocking the right side of our lane. [01:49:44] Nudge left due to the truck blocking the right side of our lane. [01:49:45] Nudge left due to the truck blocking the right side of our lane. [01:49:46] Nudge left due to the truck blocking the right side of our lane. [01:49:47] Nudge left due to the truck blocking the right side of our lane. [01:49:48] Nudge left due to the truck blocking the right side of our lane. [01:49:49] Nudge left due to the truck blocking the right side of our lane. [01:49:50] Nudge left due to the truck blocking the right side of our lane. [01:49:52] Nudge left due to the truck blocking the right side of our lane. [01:49:55] Nudge left due to the truck blocking the right side of our lane. [01:49:57] Nudge left due to the truck blocking the right side of our lane. [01:49:58] Nudge left due to the truck blocking the right side of our lane. [01:49:59] Nudge left due to the truck blocking the right side of our lane. [01:50:00] Nudge left due to the truck blocking the right side of our lane. [01:50:01] Nudge left due to the truck blocking the right side of our lane. [01:50:02] Nudge left due to the truck blocking the right side of our lane. [01:50:03] Nudge left due to the truck blocking the right side of our lane. [01:50:04] Nudge left due to the truck blocking the right side of our lane. [01:50:05] Nudge left due to the truck blocking the right side of our lane. [01:50:06] Nudge left due to the truck blocking the right side of our lane. [01:50:07] Nudge left due to the truck blocking the right side of our lane. [01:50:09] Nudge left due to the truck blocking the right side of our lane. [01:50:11] Nudge left due to the truck blocking the right side of our lane. [01:50:12] Nudge left due to the truck blocking the right side of our lane. [01:50:13] Nudge left due to the truck blocking the right side of our lane. [01:50:14] Nudge left due to the truck blocking the right side of our lane. [01:50:15] Nudge left due to the truck blocking the right side of our lane. [01:50:16] Nudge left due to the truck blocking the right side of our lane. [01:50:17] Nudge left due to the truck blocking the right side of our lane. [01:50:18] Nudge left due to the truck blocking the right side of our lane. [01:50:19] Nudge left due to the truck blocking the right side of our lane. [01:50:20] Nudge left due to the truck blocking the right side of our lane. [01:50:22] Nudge left due to the truck blocking the right side of our lane. [01:50:25] Nudge left due to the truck blocking the right side of our lane. [01:50:27] Nudge left due to the truck blocking the right side of our lane. [01:50:28] Nudge left due to the truck blocking the right side of our lane. [01:50:29] Nudge left due to the truck blocking the right side of our lane. [01:50:30] Nudge left due to the truck blocking the right side of our lane. [01:50:31] Nudge left due to the truck blocking the right side of our lane. [01:50:32] Nudge left due to the truck blocking the right side of our lane. [01:50:33] Nudge left due to the truck blocking the right side of our lane. [01:50:34] Nudge left due to the truck blocking the right side of our lane. [01:50:35] Nudge left due to the truck blocking the right side of our lane. [01:50:36] Nudge left due to the truck blocking the right side of our lane. [01:50:37] Nudge left due to the truck blocking the right side of our lane. [01:50:40] Nudge left due to the truck blocking the right side of our lane. [01:50:42] Nudge left due to the truck blocking the right side of our lane. [01:50:43] Nudge left due to the truck blocking the right side of our lane. [01:50:44] Nudge left due to the truck blocking the right side of our lane. [01:50:45] Nudge left due to the truck blocking the right side of our lane. [01:50:46] Nudge left due to the truck blocking the right side of our lane. [01:50:47] Nudge left due to the truck blocking the right side of our lane. [01:50:48] Nudge left due to the truck blocking the right side of our lane. [01:50:49] Nudge left due to the truck blocking the right side of our lane. [01:50:50] Nudge left due to the truck blocking the right side of our lane. [01:50:51] Nudge left due to the truck blocking the right side of our lane. [01:50:52] Nudge left due to the truck blocking the right side of our lane. [01:50:55] Nudge left due to the truck blocking the right side of our lane. [01:50:57] Nudge left due to the truck blocking the right side of our lane. [01:50:58] Nudge left due to the truck blocking the right side of our lane. [01:50:59] Nudge left due to the truck blocking the right side of our lane. [01:51:00] Nudge left due to the truck blocking the right side of our lane. [01:51:01] Nudge left due to the truck blocking the right side of our lane. [01:51:02] Nudge left due to the truck blocking the right side of our lane. [01:51:03] Nudge left due to the truck blocking the right side of our lane. [01:51:04] Nudge left due to the truck blocking the right side of our lane. [01:51:05] Nudge left due to the truck blocking the right side of our lane. [01:51:06] Nudge left due to the truck blocking the right side of our lane. [01:51:07] Nudge left due to the truck blocking the right side of our lane. [01:51:11] Nudge left due to the truck blocking the right side of our lane. [01:51:12] Nudge left due to the truck blocking the right side of our lane. [01:51:13] Nudge left due to the truck blocking the right side of our lane. [01:51:14] Nudge left due to the truck blocking the right side of our lane. [01:51:15] Nudge left due to the truck blocking the right side of our lane. [01:51:16] Nudge left due to the truck blocking the right side of our lane. [01:51:17] Nudge left due to the truck blocking the right side of our lane. [01:51:18] Nudge left due to the truck blocking the right side of our lane. [01:51:19] Nudge left due to the truck blocking the right side of our lane. [01:51:20] Nudge left due to the truck blocking the right side of our lane. [01:51:21] Nudge left due to the truck blocking the right side of our lane. [01:51:22] Nudge left due to the truck blocking the right side of our lane. [01:51:25] Nudge left due to the truck blocking the right side of our lane. [01:51:27] Nudge left due to the truck blocking the right side of our lane. [01:51:28] Nudge left due to the truck blocking the right side of our lane. [01:51:29] Nudge left due to the truck blocking the right side of our lane. [01:51:30] Nudge left due to the truck blocking the right side of our lane. [01:51:31] Nudge left due to the truck blocking the right side of our lane. [01:51:32] Nudge left due to the truck blocking the right side of our lane. [01:51:33] Nudge left due to the truck blocking the right side of our lane. [01:51:34] Nudge left due to the truck blocking the right side of our lane. [01:51:35] Nudge left due to the truck blocking the right side of our lane. [01:51:36] Nudge left due to the truck blocking the right side of our lane. [01:51:37] The stars had them in Hollywood. Now we all got teams making dreams come true. Building companies from living rooms. But they need so much compute. We hear ya. That's why we created Vera. Ruben's store, the show is true. The cheapest tokens coming through. Ten times faster, inference heaven. More special agents than 007. Bluefield keeps agents memory true. Now let's talk about its CPU. 50% faster, that's outrageous. Not for Vera. [01:52:07] It's built for agents. Envylink fusion blends ASIC smartly. Everyone's welcome to the Envylink party. Well, if you like that introduction. Vera Rubens in full production. Memotron Ultra leads the run. 5x faster, work gets done. Memo Claw keeps the guardrails right. Open shell keeps the sandbox tight. Your code migrated and reviewed. All before this song is through. Yeah. It's a five layer cake. [01:52:36] Make no mistake. Global AI clouds build lots of gigawatts. DSX keeps power lean. Connecting dots. Every watt optimized for you. So you can have your cake. Can eat it too. RTX spark is finally here. Biggest PC moment in 40 years. Agents powering our workflows. Running anywhere Windows goes. [01:52:58] Harnesses run on CPU. [01:52:59] Harnesses run on CPU. Models fly on GPU. Cosmos builds worlds that robots need. Turning compute into synthetic feet. Alpha meiosis and reasons through. Understands roads like people do. Who is how they learn to move. Learning skills and finding ground. Unitries powered by thought. The future's humanoid. Count on more. [01:53:24] Oh, yeah, we love you. [01:53:54] The future's bright. Come see what's next. [01:54:00] Thank you, Taiwan. [01:54:03] Welcome to Computex. [01:54:14] Have a great Computex. [01:54:19] Thanks for an amazing year. [01:54:22] Thank you for all your friendship and support. [01:54:24] Thank you. Take care. [01:54:26] Have a great Computex. [01:54:28] Have a great Computex. [01:54:31] Have a great Computex. [01:54:32] Have a great Computex. [01:54:35] Woke up feeling something shift, same room but the air felt thick, mirror said I'm still [01:54:49] that kid, I said you're bigger than this, tied my laces, hands still shook, first step [01:54:55] was the hardest one, every lap, every sideways, look all fade when the races run, I carried