NVIDIA GTC Taipei 2026 Keynote — Full Replay

[00:00:00] This is how intelligence is made, a new kind of factory, generator of tokens, the building [00:00:18] blocks of AI. Tokens have opened a new frontier, turning data into knowledge, reason, action. [00:00:36] They reveal patterns in complexity we could never see, mirror our cities to keep us safe, [00:00:57] and lift us high above them. Tokens help robots learn from us, work alongside us. [00:01:22] They go where we cannot, lending us, helping hands, and closing the gap between hope and [00:01:38] healing so that we breathe easier, and the smallest hearts beat stronger. [00:01:52] Tokens are helping us break new ground. On a scale never attempted. [00:01:59] Tokens are helping us break new ground. On a scale never attempted. [00:02:06] So we can reach star cloud one, separation confirmed. To infinity and beyond. [00:02:13] To infinity and beyond. [00:02:20] Together, we take the next great leap. To infinity and beyond. [00:02:28] And beyond. [00:02:29] Together, we take the next great leap. Into a bright new future. Built for all mankind. [00:02:35] And here, in Taipei, is where it all begins. [00:02:36] And here, in Taipei, is where it all begins. [00:02:36] And here, in Taipei, is where it all begins. [00:02:36] And here, in Taipei, is where it all begins. [00:02:37] Together, we take the next great leap. Into a bright new future. [00:02:38] And here, in Taipei, is where it all begins. [00:02:40] Together, we take the next great leap. Into a bright new future. [00:02:42] Together, we take the next great leap. Into a bright new future. [00:02:49] Together, we take the next great leap. And here, in Taipei, we take the next great leap. [00:02:55] And here, in Taipei, we take the next great leap. And here, in Taipei, we take the next great leap. [00:03:01] And here, in Taipei, is where it all begins. [00:03:14] Welcome to the stage, NVIDIA founder and CEO, Jensen Wang. [00:03:26] Welcome to G.T.C. Taiwan. [00:03:35] So great to see all of you. [00:03:38] Very good to be home. [00:03:40] I brought my parents home. Where are my parents? [00:03:42] Everybody give a round of applause to my mom and dad. [00:03:52] And a round of applause for our pre-game show, Superstars, ladies and gentlemen. [00:04:05] Look how adorable they are. [00:04:08] The Superstars of Taiwan. [00:04:10] There are so many of you here today. [00:04:12] We are broadcasting this right now to 70 other watch parties across Taiwan. [00:04:20] 70 different conferences are going at the same time. [00:04:24] Everybody is watching this keynote. [00:04:26] We have so much to tell you. [00:04:28] And I have so many partners to thank. [00:04:30] It is incredible how large our ecosystem in Taiwan has become. [00:04:35] Most of the time, when people think about ecosystem, they think about our software stack. [00:04:41] They think about the developer ecosystem above the computing systems that NVIDIA builds. [00:04:46] But NVIDIA's ecosystem spans all the way upstream to all of our supply chain. [00:04:54] Here in Taiwan, where it all begins. [00:04:56] And downstream all the way to data centers and eventually to end users. [00:05:02] Today, we are going to talk about almost all of the ecosystem. [00:05:06] There are so many people to thank. [00:05:08] I love my ecosystem here. [00:05:10] I mean, there are so many companies here. [00:05:14] And some of my favorite ecosystem partners. [00:05:16] I mean, there are so many people to do. [00:05:26] I mean, there are so many people to do. [00:05:28] I mean, there are so many people to do. [00:05:30] I mean, there are so many people to do. [00:05:32] I mean, there are so many people to do. [00:05:36] I mean, there are so many people to do. [00:05:38] I mean, there are so many people to do. [00:05:40] I mean, there are so many people to do. [00:05:42] I mean, there are so many people to do. [00:05:44] I mean, there are so many people to do. [00:05:46] I mean, there are so many people to do. [00:05:48] There are so many people to do. [00:05:49] There are so many people to do. [00:05:50] There are so many people to do. [00:05:51] There are so many people to do. [00:05:52] There are so many people to do. [00:05:53] There are so many people to do. [00:05:54] There are so many people to do. [00:05:55] There are so many people to do. [00:05:56] There are so many people to do. [00:05:57] There are so many people to do. [00:05:58] There are so many people to do. [00:05:59] There are so many people to do. [00:06:00] There are so many people to do. [00:06:01] There are so many people to do. [00:06:02] There are so many people to do. [00:06:03] There are so many people to do. [00:06:04] There are so many people to do. [00:06:05] There are so many people to do. [00:06:06] There are so many people to do. [00:06:07] There are so many people to do. [00:06:08] There are so many people to do. [00:06:09] There are so many people to do. [00:06:18] There are so many people to do. [00:06:19] There are so many people to do. [00:06:21] There are so many people to do. [00:06:22] There are so many people to do. [00:06:23] There are so many people to do. [00:06:24] There are so many people to do. [00:06:25] There are so many people to do. [00:06:26] There are so many people to do. [00:06:27] There are so many people to do. [00:06:28] There are so many people to do. [00:06:29] There are so many people to do. [00:06:30] There are so many people to do. [00:06:31] There are so many people to do. [00:06:32] There are so many people to do. [00:06:33] There are so many people to do. [00:06:34] There are so many people to do. [00:06:35] There are so many people to do. [00:06:36] There are so many people to do. [00:06:37] There are so many people to do. [00:06:38] There are so many people to do. [00:06:39] There are so many people to do. [00:06:40] There are so many people to do. [00:06:48] There are so many people to do. [00:06:49] There are so many people to do. [00:06:50] There are so many people to do. [00:06:51] There are so many people to do. [00:06:52] There are so many people to do. [00:06:53] There are so many people to do. [00:06:54] There are so many people to do. [00:06:55] There are so many people to do. [00:06:56] There are so many people to do. [00:06:57] There are so many people to do. [00:06:58] There are so many people to do. [00:06:59] There are so many people to do. [00:07:00] There are so many people to do. [00:07:01] There are so many people to do. [00:07:02] There are so many people to do. [00:07:03] There are so many people to do. [00:07:04] There are so many people to do. [00:07:05] There are so many people to do. [00:07:06] There are so many people to do. [00:07:07] There are so many people to do. [00:07:08] There are so many people to do. [00:07:09] There are so many people to do. [00:07:18] There are so many people to do. [00:07:19] There are so many people to do. [00:07:20] There are so many people to do. [00:07:21] There are so many people to do. [00:07:22] There are so many people to do. [00:07:23] There are so many people to do. [00:07:24] There are so many people to do. [00:07:25] There are so many people to do. [00:07:26] There are so many people to do. [00:07:27] There are so many people to do. [00:07:28] There are so many people to do. [00:07:29] There are so many people to do. [00:07:30] There are so many people to do. [00:07:31] There are so many people to do. [00:07:32] There are so many people to do. [00:07:33] There are so many people to do. [00:07:34] There are so many people to do. [00:07:35] There are so many people to do. [00:07:36] There are so many people to do. [00:07:37] There are so many people to do. [00:07:38] There are so many people to do. [00:07:39] There are so many people to do. [00:07:48] 500 million commits. [00:07:51] In the first few months. [00:07:53] In the first few months. [00:07:55] Of 2026. [00:07:57] It has nearly tripled. [00:07:59] Now what does that mean? [00:08:01] 30 million software developers. [00:08:04] Representing. [00:08:05] About 3 trillion dollars. [00:08:08] Worth of GDP. [00:08:10] Producing 3. [00:08:11] That's what they're paid. [00:08:13] 3 trillion dollars worth of salaries per year. [00:08:16] Which is generating. [00:08:18] Economic growth. [00:08:19] For the rest of the industries. [00:08:21] Say 100 trillion dollars. [00:08:23] Of the world's industries. [00:08:24] Is impacted. [00:08:25] Is generated by. [00:08:27] 3 billion dollars. [00:08:29] Worth of salary. [00:08:30] That 3 trillion dollars. [00:08:32] Excuse me. [00:08:33] 3 trillion. [00:08:34] That 3 trillion dollars. [00:08:35] Worth of salary. [00:08:36] Is now producing. [00:08:37] Nearly 3 times. [00:08:39] As much output. [00:08:41] It's effectively. [00:08:43] A 9 trillion dollar. [00:08:45] Productivity. [00:08:47] From 3. [00:08:48] Trillion dollars. [00:08:49] Of salaries. [00:08:50] Does that make any sense? [00:08:52] The difference. [00:08:53] Is absolutely extraordinary. [00:08:55] This is the potential. [00:08:56] This is the promise of AI. [00:08:58] The number of engineers. [00:08:59] Software engineers. [00:09:00] Is actually increasing. [00:09:01] People talk about. [00:09:02] AI reducing jobs. [00:09:04] Complete nonsense. [00:09:05] It's. [00:09:06] Causing more software engineers. [00:09:08] To be hired. [00:09:09] And the reason for that. [00:09:10] Is very simple. [00:09:11] If you can hire. [00:09:12] A software engineer. [00:09:13] And you could generate. [00:09:15] 9 trillion dollars. [00:09:17] Worth of. [00:09:18] Productive work. [00:09:19] Why would you want to hire. [00:09:20] More software engineers? [00:09:21] If that line. [00:09:24] Was flat. [00:09:25] Then. [00:09:26] Obviously. [00:09:27] People will hire. [00:09:28] Fewer software engineers. [00:09:29] But because the output. [00:09:30] Is so incredible. [00:09:31] People want to hire. [00:09:32] More software engineers. [00:09:33] More software engineers. [00:09:34] This is going to show up. [00:09:35] In our economy. [00:09:36] Somehow. [00:09:36] Soon. [00:09:37] And so. [00:09:38] The first thing is. [00:09:39] Useful AI has arrived. [00:09:40] Now. [00:09:41] What does that mean? [00:09:42] From the industry's perspective? [00:09:43] From the industry's perspective. [00:09:45] That means. [00:09:46] That tokens. [00:09:47] Are now. [00:09:48] In extraordinary demand. [00:09:50] Because if you could do this. [00:09:51] You're going to want. [00:09:52] To produce more of it. [00:09:53] And because tokens. [00:09:54] Are now. [00:09:55] Profitable units. [00:09:56] Of revenues. [00:09:57] Because it is now. [00:09:58] Profitable. [00:09:59] Profitable units. [00:10:00] Of revenues. [00:10:01] Because it is now. [00:10:02] Profitable. [00:10:03] The AI companies. [00:10:04] Want to build. [00:10:05] A lot more. [00:10:06] Tokens. [00:10:07] Generate a lot more. [00:10:08] Tokens. [00:10:09] Build more AI factories. [00:10:10] Which is the reason why. [00:10:11] Compute demand. [00:10:12] Here in Taiwan. [00:10:13] Has skyrocketed. [00:10:15] It is precisely. [00:10:17] The reason. [00:10:18] Why all of you. [00:10:19] Are so busy. [00:10:20] And your businesses. [00:10:21] Are doing so well. [00:10:22] In fact. [00:10:23] That looks like. [00:10:24] Some of your stock price. [00:10:25] The. [00:10:26] Compute. [00:10:27] Pattern. [00:10:28] Has changed. [00:10:29] Everything. [00:10:30] Has changed. [00:10:31] So. [00:10:32] The first. [00:10:34] Idea. [00:10:35] Is that. [00:10:36] Useful. [00:10:36] AI. [00:10:37] Has arrived. [00:10:38] AI. [00:10:38] Is now. [00:10:39] A profit. [00:10:40] Generator. [00:10:40] AI. [00:10:41] Is now. [00:10:41] A GDP. [00:10:42] Generator. [00:10:43] That's behind it. [00:10:44] Is a whole new. [00:10:45] Kind of computing pattern. [00:10:46] Not just a large. [00:10:47] Language model. [00:10:48] But an agent. [00:10:49] Today. [00:10:50] Is now. [00:10:51] A GDP. [00:10:52] Is now. [00:10:53] A GDP. [00:10:54] Generator. [00:10:54] An agent. [00:10:55] Today. [00:10:56] Almost everything. [00:10:57] We're going to talk about. [00:10:58] Is going to be based on this. [00:10:59] So let me take a quick moment. [00:11:01] And show you what I'm talking about. [00:11:03] Inside. [00:11:04] And this is a. [00:11:05] This is an agent. [00:11:06] It's an agent. [00:11:07] Application. [00:11:08] In the old days. [00:11:10] This would be. [00:11:11] Application. [00:11:12] In the old days. [00:11:13] This would be. [00:11:14] Code. [00:11:15] And this would be. [00:11:16] Operating system. [00:11:19] Application. [00:11:20] Code. [00:11:21] Running inside. [00:11:22] An application. [00:11:23] Inside an operating system. [00:11:24] Today. [00:11:25] It is agent. [00:11:26] Which consists of. [00:11:27] A large language model. [00:11:29] Or many. [00:11:30] Sitting inside a harness. [00:11:33] And that harness. [00:11:34] Helps it. [00:11:35] Orchestrates it. [00:11:36] To do. [00:11:37] Productive work. [00:11:38] This is the input. [00:11:40] When that input comes. [00:11:42] It has to understand. [00:11:44] Observe. [00:11:45] Reason. [00:11:46] Act. [00:11:47] Use tools. [00:11:48] Use tools. [00:11:49] That tool could be. [00:11:50] A spreadsheet. [00:11:51] Web browser. [00:11:52] A data processing engine. [00:11:54] Database engine. [00:11:55] For example. [00:11:56] This. [00:11:57] Is orchestrated. [00:11:59] This harness. [00:12:00] Orchestrate. [00:12:01] This. [00:12:02] Routing of information. [00:12:04] Every single time it touches. [00:12:06] Either processing the context. [00:12:08] Understanding what is happening. [00:12:10] Reasoning about what to do. [00:12:13] Coming up with a plan. [00:12:15] That you can act. [00:12:16] That it acts on. [00:12:17] That orchestration path. [00:12:20] Is orchestrated by. [00:12:21] Some software. [00:12:22] And so this is fundamentally. [00:12:24] A agent. [00:12:26] It deals with short term memory. [00:12:29] Called working memory. [00:12:30] Long term memory. [00:12:31] Just like we do. [00:12:32] We have long term memory. [00:12:33] And so the memory management system. [00:12:35] Is incredibly important. [00:12:37] This entire system. [00:12:39] Is called an agent. [00:12:41] The large language model. [00:12:43] Is used. [00:12:44] To do the thinking. [00:12:45] To thinking. [00:12:46] And the harness. [00:12:48] Connects everything together. [00:12:50] Just like an operating system. [00:12:51] Okay. [00:12:52] And so this is the new computing model. [00:12:55] And this is what an agent. [00:12:56] It could do incredible things. [00:12:58] This is the big breakthrough. [00:13:00] The simultaneous convert. [00:13:02] The convergence of. [00:13:03] Large language models. [00:13:04] That are now able to. [00:13:06] Do a really good job. [00:13:07] Thinking. [00:13:08] Reasoning. [00:13:09] Planning. [00:13:10] Using tools. [00:13:11] And the fact that we have now. [00:13:12] These harnesses. [00:13:13] That manages memory. [00:13:15] The orchestration. [00:13:16] Uses tools. [00:13:18] We can now do amazing things. [00:13:20] Let me give you some example. [00:13:21] This is. [00:13:22] This is a prompt. [00:13:23] This is the prompt. [00:13:24] This is the code that is generated. [00:13:29] And this comes out. [00:13:32] This is the input. [00:13:35] This is the input. [00:13:37] And that's the output. [00:13:40] Do you guys. [00:13:41] What do you guys think? [00:13:42] It's pretty amazing, right? [00:13:47] We use cloud code here. [00:13:48] But CodeX. [00:13:49] It does an incredible job as well. [00:13:50] Here's another example. [00:13:51] This is the input. [00:13:52] Create a GIF. [00:13:53] NVIDIA. [00:13:54] Green dots. [00:13:55] On black. [00:13:56] Scatter. [00:13:57] Form. [00:13:58] Taiwan 101. [00:14:01] Building. [00:14:02] Morph to GTC. [00:14:03] Taipei. [00:14:04] 2026. [00:14:05] Morph to NVIDIA. [00:14:06] I logo. [00:14:07] Then scatter and repeat. [00:14:08] Right? [00:14:09] So you saw that. [00:14:10] That was the prompt. [00:14:11] Here's the next one. [00:14:12] I lost my remote control battery clip. [00:14:15] It looks like this. [00:14:16] Create a CAD file. [00:14:19] It uses a tool. [00:14:20] Create a CAD file. [00:14:21] Ready for 3D printing to create a new one. [00:14:25] Make sense? [00:14:26] This is now the new computing pattern. [00:14:29] Whereas we used to launch an application. [00:14:33] Click and type. [00:14:36] We now replace that with explaining to the AI what we want. [00:14:41] Our intent. [00:14:42] The AI generates the code. [00:14:45] Or uses tools. [00:14:46] And produce the necessary output. [00:14:49] This is. [00:14:51] How computers are going to work in the future. [00:14:53] This is agentic AI. [00:14:56] For two years we've been building towards this. [00:14:58] And now it has arrived. [00:15:00] Now one of the big breakthroughs of course. [00:15:02] Is tool use. [00:15:03] Is tool use. [00:15:04] A lot of people have said. [00:15:05] You know Jensen AI is coming agentic AI is coming. [00:15:09] Therefore all of the software companies are going to go out of business. [00:15:12] I said it's exactly the opposite. [00:15:14] Because there are going to be so many agents. [00:15:17] The world is no longer limited by the number of people. [00:15:21] Therefore. [00:15:22] Those agents are going to use more tools than ever. [00:15:27] This is actually an incredible time to be a software company. [00:15:31] But the software has to be presented to the agent. [00:15:35] In a way that the agent can use it. [00:15:38] This is a break big breakthrough. [00:15:40] And in fact what we have done. [00:15:41] As you know. [00:15:42] What Nvidia's treasure is. [00:15:45] Is all of our CUDA libraries. [00:15:47] I call them CUDAX libraries. [00:15:49] This is Nvidia's treasure. [00:15:51] Today. [00:15:52] We're able to now. [00:15:54] Present these CUDAX libraries. [00:15:57] To agents. [00:15:58] Who can use it much more effectively. [00:16:01] Than even humans. [00:16:02] And so. [00:16:03] This is a wonderful time for CUDAX libraries. [00:16:05] Let's take a look. [00:16:10] 20 years ago. [00:16:11] We built CUDA. [00:16:12] A single architecture for accelerated computing. [00:16:16] We reinvented computing. [00:16:17] A thousand CUDAX libraries. [00:16:20] Help developers make breakthroughs. [00:16:22] In every field of science and engineering. [00:16:25] CUDAX libraries. [00:16:26] Are tools for agents. [00:16:28] CULitho. [00:16:29] For computational lithography. [00:16:32] CUOpt. [00:16:33] For decision optimization. [00:16:36] CUDSS. [00:16:37] For direct sparse solvers. [00:16:42] AIQ. [00:16:43] For deep research across structured and unstructured documents. [00:16:47] Arial. [00:16:48] For AI-RAN. [00:16:50] Warp. [00:16:51] For differentiable physics. [00:16:54] Parabricks. [00:16:55] For genomics. [00:16:56] At their foundation. [00:16:57] Our algorithms. [00:16:58] And they. [00:16:59] Are beautiful. [00:17:00] At their foundation. [00:17:01] Our algorithms. [00:17:02] And they. [00:17:03] Are beautiful. [00:17:04] Are beautiful. [00:17:06] To be continued. [00:17:07] To be continued. [00:17:08] To be continued. [00:17:09] To be continued. [00:17:10] To be continued. [00:17:11] To be continued. [00:17:12] To be continued. [00:17:13] To be continued. [00:17:14] To be continued. [00:17:15] To be continued. [00:17:16] To be continued. [00:17:17] To be continued. [00:17:18] To be continued. [00:17:19] To be continued. [00:17:20] To be continued. [00:17:21] To be continued. [00:17:23] To be continued. [00:17:24] To be continued. [00:17:25] To be continued. [00:17:26] To be continued. [00:17:27] To be continued. [00:17:28] To be continued. [00:17:29] To be continued. [00:17:30] To be continued. [00:17:31] to be continued. [00:17:32] to be continued. [00:18:02] To be continued. [00:18:03] To be continued. [00:18:04] To be continued. [00:18:05] To be continued. [00:18:06] To be continued. [00:18:07] To be continued. [00:18:08] To be continued. [00:18:09] To be continued. [00:18:10] To be continued. [00:18:11] To be continued. [00:18:12] To be continued. [00:18:13] To be continued. [00:18:14] To be continued. [00:18:15] To be continued. [00:18:16] To be continued. [00:18:17] To be continued. [00:18:18] To be continued. [00:18:19] To be continued. [00:18:20] To be continued. [00:18:21] To be continued. [00:18:22] To be continued. [00:18:23] To be continued. [00:18:24] To be continued. [00:18:25] To be continued. [00:18:26] To be continued. [00:18:27] To be continued. [00:18:28] To be continued. [00:18:58] To be continued. [00:19:28] To be continued. [00:19:29] To be continued. [00:19:30] To be continued. [00:19:31] To be continued. [00:19:32] To be continued. [00:19:33] A round of applause for math. [00:20:00] Math is beautiful. [00:20:03] The computing pattern of software is going to change. [00:20:12] In fact, let's come back to this. [00:20:15] This is the agent. [00:20:16] It is the ultimate disaggregated and distributed computing model. [00:20:25] So many different computers are going to be activated in order to process this agent. [00:20:30] The agent consists of model, harness, tools and skills, and a runtime. [00:20:41] All of that is running at different places in a data center. [00:20:46] You can think of the model as the brain. [00:20:50] The harness as the body. [00:20:53] The tools that it uses working in a runtime. [00:21:00] Think of it as a workshop. [00:21:01] So this is a person, a worker working with tools in a workshop. [00:21:06] Of course, this is being done at extraordinarily large scales. [00:21:11] And each one of those steps are running in a different part of the computer. [00:21:16] And you could see the large language model is thinking, context processing, observing, understanding the environment, reasoning, coming up with a plan and acting on the plan. [00:21:30] Every single time that happens, an entire rack of Grace Blackwell NVLink 72 is activated. [00:21:38] It's thinking with the large language model. [00:21:41] Whenever it uses a tool, a CPU is used. [00:21:45] That tool could be a C compiler. [00:21:48] It could be Python. [00:21:49] It could be JavaScript. [00:21:50] Or it could be accelerated computing. [00:21:53] Today's agents are relatively simple users of tools. [00:21:58] 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. [00:22:08] They solve some of the most important problems the world knows. [00:22:12] And all of our CUDAX libraries are now going to come with skills that the AI could learn how to use. [00:22:21] So the CUDAX library, some skills, basically a manual, the AI reads it and go, "Aha, that's how you use it." [00:22:31] The ability to use these libraries by agents are going to be incredible. [00:22:36] And so the tools run on CPUs and GPUs and large language models. [00:22:41] The security harness runs on CPUs and a security processor called the DPU, NVIDIA's Bluefield. [00:22:50] The orchestration of all this runs on a CPU. [00:22:53] This is the entire harness, and the CPU is orchestrating all of the work. [00:22:58] One of the hardest parts is memory. [00:23:01] You could just imagine. [00:23:02] The working memory is called KV caching. [00:23:05] What to remember, compaction, not just compression, but how to retrieve. [00:23:11] Do you retrieve structured data? [00:23:13] Do you retrieve unstructured data? [00:23:15] What is the ontology, the relationship of all of these different data to itself? [00:23:21] That entire processing is incredibly complicated. [00:23:24] The memory system, the memory system of AIs is going to cause the storage system to be completely revolutionized. [00:23:33] 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. [00:23:48] A whole bunch of software sitting inside a binary, sitting inside an operating system. [00:23:53] This is the reason this disaggregated, this distributed, this heterogeneous computing problem is precisely the reason we built our next generation. [00:24:06] Verarubin. [00:24:07] Verarubin. [00:24:08] Verarubin is not one chip. [00:24:11] Verarubin is not a GPU only. [00:24:15] It starts with the GPU. [00:24:17] But Verarubin is incredible. [00:24:20] This entire thing is Verarubin. [00:24:24] From end to end. [00:24:27] It has GPUs, Verarubin, NVLink 72. [00:24:31] It is orchestrated by Verarubin CPUs that I'm going to tell you more about. [00:24:35] The storage systems, Revolutionary, Verarubin, along with CX9, our software stack called Doka, the security processor that's inside so that everything is encrypted at rest, in motion, as well as in use. [00:24:56] Everything across this is secure because the AI model is so precious. [00:25:01] This is the reason why this entire system obeys confidential computing. [00:25:06] Each one of these systems would be a complete revolution in itself. [00:25:11] Verarubin is the most ambitious endeavor in the history of our company. [00:25:17] The whole company worked on Verarubin across all 40,000 engineers, not to mention all of you. [00:25:25] All of you participated in the creation of this entire system. [00:25:29] Verarubin is really a miracle. [00:25:32] And it's not just one chip, it is so many. [00:25:34] Well, it's even beyond that. [00:25:37] A long time ago, NVIDIA used to be a GPU company. [00:25:41] But over the years we've evolved to become a systems company. [00:25:47] You're looking here now for the most complex systems, most complex and ground up system ever designed. [00:25:54] But ultimately, our customers, our partners don't want to buy computers. [00:26:01] They want to build AI factories, which is the reason why NVIDIA has really started to transform ourselves yet again. [00:26:09] You could see so much of our technology is now at the entire infrastructure scale. [00:26:15] Our partners are at infrastructure scale, power generators, cooling systems, the grid providers. [00:26:24] So many industrial companies are now part of our ecosystem. [00:26:29] Because ultimately, we're trying to build an entire stack, just like GPUs. [00:26:34] Just like when we were building Grace Blackwall NVLink 72. [00:26:39] Just like now, we are building a full stack system so that our customers could build amazing AI infrastructure. [00:26:48] Let's take a look. [00:26:51] The world is racing to build AI factories. [00:26:54] The largest infrastructure build out in human history. [00:26:57] AI factories are incredibly complex. [00:27:00] Every layer, chip, rack, network, power, cooling, grid, must be designed together from end to end. [00:27:08] Because compute is revenues. [00:27:13] NVIDIA DSX is the blueprint. [00:27:15] A reference design for building and operating AI factories at maximum efficiency and profitability. [00:27:22] It starts with DSX SIM. [00:27:25] With the DSX SIM Omniverse Blueprint, partners design and validate an NVIDIA Vera Rubin AI factory. [00:27:30] Before a single rack lands. [00:27:32] They plan the layout. [00:27:35] Simulate the power and cooling. [00:27:40] Design the network. [00:27:43] Validate every integration. [00:27:45] Test every change in the digital twin. [00:27:48] The factory powers on. [00:27:51] DSX OS takes over and provisions, operates, monitors, and remediates the infrastructure. [00:27:58] Turning the installed systems into trusted, multi-tenant, resilient, AI-ready capacity. [00:28:07] Today's AI factories over provision power by up to 40%. [00:28:11] DSX MAX LPS lets operators safely deploy more GPUs inside the same power budget. [00:28:18] Adding billions in annual revenue. [00:28:24] Breakthrough hot liquid cooling at 45 degrees Celsius uses less water and energy. [00:28:30] More power going to revenue-generating compute. [00:28:33] Incredible. [00:28:35] Dynamic power allocation steers power from rack to rack, recovering stranded watts, sending them where work is happening. [00:28:42] In-rack power smoothing flattens peak current spikes and power surges. [00:28:49] Throughout the factory, teams of AI agents work with DSX MAX LPS, continuously coordinating to balance cooling and power to meet workload demand. [00:29:02] DSX AI factories are flexible energy assets that operate cooperatively with the grid. [00:29:09] DSX Flex reads real-time grid signals and dynamically adjusts factory power when the grid needs relief. [00:29:16] A hundred gigawatts of AI factories will come online before the end of the decade. [00:29:24] NVIDIA DSX AI factories run at highest efficiency, produce the lowest cost tokens, and make the grid stronger. [00:29:41] 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. [00:29:57] That was a computing ecosystem. [00:30:00] This is an AI factory ecosystem. [00:30:03] This is way downstream of all of you. [00:30:06] Upstream of me is all of you, and downstream of us is this ecosystem. [00:30:12] Because NVIDIA ultimately is not just building a GPU, not just building a system. [00:30:18] We're helping customers build these AI factories, these AI infrastructure that is so immensely complex. [00:30:24] Each one of these at one gigawatt level started at 30, 20, 30 billion dollars. [00:30:33] It is at 50, 60 billion dollars. [00:30:35] And soon it will be 80, 100 billion dollars per gigawatt. [00:30:41] 100 billion dollars into an AI factory. [00:30:46] It must work the first time and it must work right away. [00:30:50] The cost of capital is incredible. [00:30:52] The complexity is incredible. [00:30:54] So as you see, we used to design a chip inside a computer. [00:31:00] And then we simulated a system inside a computer. [00:31:04] It must work right now. [00:31:05] Today, you saw just now, everything was built in Omniverse. [00:31:10] I've been working with Omniverse with all of you for a long time. [00:31:14] This was the dream come true. [00:31:16] So that we can build these gigantic systems as large as the world wants to build. [00:31:22] Inside a digital framework, inside a digital simulator, in a digital world. [00:31:28] Long before we build the first break ground and put our money to work. [00:31:33] So this is our ecosystem, we call it DSX. [00:31:38] RTX is for our GPU. [00:31:40] DGX is for our systems. [00:31:42] And now DSX, basically infrastructure. [00:31:45] Because of the work that we do here, across this entire stack, including our systems and software. [00:31:51] It's the reason why we can work with small companies and enable them to be world-class AI clouds. [00:31:58] Every one of these I'm about to show you are small companies just recently. [00:32:02] And now CoreWeave is worth 50, 60, 70 billion dollars and growing incredibly fast. [00:32:09] Recently, we worked with Nebius. [00:32:11] And again, they're growing incredibly fast. [00:32:14] Each one of these clouds have incredible customers. [00:32:18] Cursor, the software coding company, Black Mountain Labs, Image Generation, World Labs, World Foundation Model, Revolut, the leading financial services AI company, and Shopify. [00:32:31] Here's another one. [00:32:32] Here's another one. [00:32:32] This is Nscale, and their customers are British Telecom, Google. [00:32:38] Google is using one of our AI clouds, thinking machines, a Frontier Labs company, which is super exciting. [00:32:46] Here's NaverCloud in Korea, Bank of Korea, Hyundai, so many incredible companies. [00:32:54] Here's one in India, Yoda, incredible companies. [00:32:59] Here's one based in Singapore, building in Australia, Together AI, AI Singapore. [00:33:06] This is one in Indonesia. [00:33:08] Each one of these companies, each one of these companies are serving regional as well as global customers. [00:33:16] AI is going to run everywhere. [00:33:18] Every company will be powered by it. [00:33:21] Every region will build it. [00:33:25] Endosat here in Indonesia. [00:33:27] Here in Taiwan, GMI. [00:33:31] Here in Taiwan, GMI. [00:33:34] It's okay to clap. [00:33:40] So, incredible companies, incredible opportunity, but all of them need several things. [00:33:49] Of course, they need the computing stack. [00:33:51] This entire stack underneath, this is what made NVIDIA famous. [00:33:55] All of our hardware and software and libraries, our connection into the world's ecosystem of third-party developers, [00:34:03] makes it possible for anyone to stand up an AI cloud. [00:34:08] Remember, the AI cloud is so complex now. [00:34:12] This is the software version. [00:34:13] This is the computer science version. [00:34:16] The money version. [00:34:18] The asset version is what I showed you earlier. [00:34:22] It's a giant factory. [00:34:24] Having this ability alone is not enough, which is the reason why NVIDIA has become an AI infrastructure company. [00:34:32] Now, doing this well and becoming incredibly good at helping customers build AI factories and deploying AI factories is incredibly important. [00:34:44] And the reason for that is this. [00:34:46] Compute is revenue now. [00:34:49] Compute is profit. [00:34:51] The absence of revenues and profit is loss. [00:34:55] And so it's really important to realize that this is when this is an example of an AI infrastructure coming online. [00:35:06] It could take, it could be coming online quickly. [00:35:09] It could take a while. [00:35:11] Its throughput could be high. [00:35:12] It could be low. [00:35:14] Its resilience and reliability could be good or bad. [00:35:18] And its lifetime of usefulness could be long or short. [00:35:22] Because this represents 50, 60, going to a hundred billion dollars. [00:35:30] This curve matters greatly. [00:35:34] Which is the reason why NVIDIA is such a great partner. [00:35:37] Working with us because of our fully integrated capability. [00:35:42] We didn't just come up with a PowerPoint slide. [00:35:45] We created the entire infrastructure. [00:35:47] We connected everything together. [00:35:49] We built out billions and billions of it ourselves to make sure that everything works well. [00:35:56] As a result of that, our time, our time to first token, our time to first token, our time to first inference. [00:36:06] Our time to training turned on is much faster. [00:36:12] Second, because our throughput per watt, our tokens per watt, is utterly world-class. [00:36:22] And the reason for that is because we integrate everything. [00:36:25] We design everything from the ground up. [00:36:26] We simulate the entire system and we use extreme co-design. [00:36:31] Just like I showed you just now with the Vera Rubin rack. [00:36:34] Everything was designed in order to deliver on this incredible throughput. [00:36:39] If your data center, if your factory has one gigawatt, it will not have more. [00:36:48] One gigawatt means one gigawatt. [00:36:51] That's all the power generation you could do. [00:36:54] If you have one gigawatt of power, then throughput per watt is revenues. [00:37:01] Because every token is profitable. [00:37:04] Every token is revenues. [00:37:07] This is the future. [00:37:09] Compute is revenues. [00:37:11] Performance per watt is your revenues. [00:37:14] Choosing the wrong architecture, just because the chips are cheaper, doesn't translate. [00:37:21] Doesn't make sense. [00:37:23] You need to make sure that your revenues per watt, the more you buy, the more you make. [00:37:29] And so tokens per watt. [00:37:32] And then lastly, very light. [00:37:35] Oh, second, third is reliability. [00:37:38] If you ever get a chance to see these data centers, there are so many moving parts, millions of cables. [00:37:44] The ability for all of those computers to work harmoniously, reliably, is extremely low. [00:37:53] It is just extremely difficult. [00:37:55] We have now been operating very large scale for a very long time. [00:37:59] That experience matters. [00:38:01] That difference, mean time between interrupts, extremely important. [00:38:07] And then lastly, this is very hard. [00:38:11] The lifetime of these systems, the lifetime of these systems, the software is changing all the time. [00:38:18] Four years ago, which is in the time of Hopper, AI has completely changed. [00:38:25] Six years ago, this is the timeframe of Ampere, AI has completely changed. [00:38:32] We started out talking about CNNs. [00:38:35] Here we are, then we talked about transformers. [00:38:38] And then we talked about mixture of experts. [00:38:40] Now we're talking about agentic systems. [00:38:43] Every single generation, every single few months, the software industry is coming up with new technology. [00:38:52] If your architecture is not flexible, if your ecosystem is not rich, then this curve cannot be long. [00:39:02] You cannot predict how long your system can last. [00:39:06] I can. [00:39:08] NVIDIA systems is all over the world. [00:39:11] Software developers start with NVIDIA CUDA. [00:39:14] And by definition, therefore, the life, the ecosystem, the useful asset is going to be much longer. [00:39:23] The difference is essentially cost. [00:39:25] You could think of it as revenues, but the other side of revenues is cost. [00:39:30] If the life of the asset is long, the TCO is low. [00:39:35] This is the difference. [00:39:37] This is what it looks like when compute. [00:39:44] The more you buy, the more you make. [00:39:54] Now, all of you are experiencing this with me. [00:39:58] Isn't that right? [00:40:00] All of your demand. [00:40:01] Your factories are working so hard. [00:40:04] Your people are working so hard all across Taiwan because everybody wants to make money. [00:40:11] They realize that AI, useful AI is here. [00:40:16] Profitable AI is here. [00:40:20] Compute demand is incredibly high and compute demand is the constraint. [00:40:25] And so let's go work super, super hard and help the world stand up AI factories everywhere. [00:40:32] This is why it's so important. [00:40:34] I'm so happy. [00:40:36] Here I am standing in front of you. [00:40:38] Vera Rubin is in full production. [00:40:46] Vera Rubin is in full production. [00:40:49] The supply chain we created for Vera Rubin is twice as large as Grace Blackwell. [00:41:01] Yeah, it's incredible. [00:41:03] And what used to take two hours to assemble one Grace Blackwell rack now only takes five minutes. [00:41:12] So not only is the capacity higher, the throughput is a lot faster. [00:41:17] And we need it all to support the demand. [00:41:21] This ecosystem is extraordinary. [00:41:24] Millions of square feet has been put online to support Grace Blackwell and preparing now, ramping up now, Vera Rubin. [00:41:33] I want to thank all of you. [00:41:34] Vera Rubin is now in full production. [00:41:36] Thank you. [00:41:41] Let's take a look. [00:41:45] Large language models generate answers. [00:41:48] Now AI agents can do work. [00:41:51] But processing agentic AI is a whole different kind of problem. [00:41:56] Agents observe, reason, plan, use tools. [00:42:00] They manage massive context, juggling working memory and long-term memory. [00:42:04] They spin up sub-agents, specialists on demand. [00:42:08] NVIDIA Vera Rubin is a multi-rack pod scale system built to process agentic AI and is now in full production. [00:42:16] The manufacturing, automation and orchestration across the supply chain, a miracle to witness. [00:42:22] Our journey started when we launched the first AI supercomputer, NVIDIA DGX1. [00:42:28] Over the next decade, we pushed every chip and system to the limit. [00:42:33] From Pascal and the first NVLink to Grace Blackwell, the first rack scale AI supercomputer. [00:42:39] And now, Vera Rubin. [00:42:41] The first multi-rack pod scale supercomputer. [00:42:44] Built for the agentic age. [00:42:46] It starts at TSMC. [00:42:48] The seven new chips that make up Vera Rubin take shape through hundreds of processing steps. [00:42:53] Three nanometer process. [00:42:55] CoWAS-R and CoWAS-L packaging. [00:42:58] HBM4 memory from Micron, SK Hynix and Samsung. [00:43:02] The Vera Rubin Compute Board. [00:43:05] Six trillion transistors with over 18,000 components on one board. [00:43:10] Vera Rubin MVL72 does the thinking, prompt and context understanding, reasoning and planning. [00:43:17] Next, a new modular compute trim. [00:43:20] Streamlined with a new PCB mid-plane design. [00:43:23] Superchips. [00:43:25] Connect X9 SuperNix. [00:43:27] And Bluefield 4 DPUs. [00:43:29] All made in place. [00:43:31] With no cables for resiliency at AI factory scale. [00:43:34] Eighteen compute trays. [00:43:36] Nine hot-swappable NVLink switch trays. [00:43:39] New high-efficiency manifolds. [00:43:41] Liquid-cooled bus bars. [00:43:43] Carrying over 5,000 amps. [00:43:45] The equivalent of 20 electric cars at full acceleration. [00:43:49] Together, 1.3 million components form this third-generation MGX rack design. [00:43:55] Congratulations to Microsoft for their operational Vera Rubin MVL72 engineering rack. [00:44:01] Congratulations to Dell and CoreWeave as well for standing up their Vera Rubin MVL72 engineering rack. [00:44:08] Then, the Vera CPU rack. [00:44:11] 256 CPUs in a single liquid-cooled rack. [00:44:15] Orchestrating the models. [00:44:17] Shuffling memory. [00:44:19] Launching tools. [00:44:21] With Quanta, Grok3 LPX takes shape. [00:44:25] 256 Grok3 LPUs across 16 trays. [00:44:29] 40 petabytes per second of SRAM bandwidth for ultra-low latency. [00:44:33] While MVL72 generates tokens at the highest throughput, [00:44:38] Grok LPX generates them at the lowest latency. [00:44:42] Vera Bluefield 4 STX, where AI keeps its memory. [00:44:46] Storage processing accelerated by Bluefield 4. [00:44:50] Connecting memory, storage, and in-silicon security. [00:44:55] And NVIDIA Spectrum X Ethernet Photonics. [00:44:59] The world's first Ethernet switch with 200-gigabit co-packaged optics. [00:45:04] TSMC's coop process. [00:45:06] Chip scale packaging. [00:45:08] And ultra-high-powered laser dies on indium phosphide. [00:45:12] Vera Rubin. [00:45:14] Five connected rack scale systems. [00:45:16] A supercomputer for AI agents. [00:45:19] 150 supply chain partners across Taiwan. [00:45:22] Millions of square feet of factory floor. [00:45:25] Hundreds of sites, chips, packages, systems, and data centers pushed to the limits of size, power, and scale. [00:45:34] This is what we call extreme code design. [00:45:37] We did this with Taiwan. [00:45:38] Together, we reinvented computing for the age of AI. [00:45:42] Taiwan was with us at the beginning. [00:45:44] And here today, as we bring Vera Rubin to the world. [00:45:48] Thank you, Taiwan. [00:45:57] Ladies and gentlemen, Vera Rubin. [00:46:00] Vera Rubin was not just built for AI. [00:46:05] Vera Rubin was not built just to run AI. [00:46:09] Vera Rubin was built to run agents. [00:46:13] This is an agentic system. [00:46:16] Imagine the complexity. [00:46:18] Which is the reason why agents is the last computer science breakthrough. [00:46:24] It has taken this many years for agents to realize its potential and become useful. [00:46:29] It stands to reason that the computer that runs it is the most advanced in the world. [00:46:34] This is Vera Rubin. [00:46:36] Let's take a look. [00:46:37] Can we bring out Vera Rubin, please? [00:46:39] Let's take a look. [00:46:59] And Janine, do we have the racks, the systems? [00:47:02] It looks heavy. [00:47:07] This is Vera Rubin. [00:47:13] Vera Rubin NVLink 72. [00:47:15] This is the Grok LPX. [00:47:18] At the next GTC, I'm going to talk to you about a lot more of this. [00:47:22] Today, we have so much to talk to you about. [00:47:25] This is Vera CPU rack, 256 CPUs, all liquid cooled. [00:47:31] Let me tell you about Vera in just a moment. [00:47:34] This is the Vera Bluefield storage processing system and also security system. [00:47:42] And of course, this is our Mellanox networking, the world's first CPO. [00:47:48] This is Vera Rubin. [00:47:50] Incredible technology all coming together. [00:47:52] Now, when we built Hopper, we built Hopper, as you know, for pre-training. [00:47:59] Pre-training was the most important application, the most important workload we were working on at the time. [00:48:05] Then when we worked on Grace Blackwell, everybody said, Jensen, you know, Nvidia is really good at pre-training. [00:48:12] Inference is so easy. [00:48:15] Do you remember that? [00:48:17] People used to say inference is so easy. [00:48:19] We could do that too. [00:48:20] But as you know, inference equals money. [00:48:24] And the models, MOEs are so complicated. [00:48:28] And to do it at incredibly high response time, fast interactivity, and high throughput at the same time is incredibly hard. [00:48:38] Which is the reason why we created NVLink 72. [00:48:41] Today, Nvidia's token cost is the lowest in the world. [00:48:46] Not by 10%, by X factors, orders of magnitude. [00:48:51] All because we did extreme co-design. [00:48:54] All because we understood the computing model, the computing pattern of inference. [00:49:00] And we were able to create NVLink 72. [00:49:03] Now, with Vera Rubin, it is beyond inference. [00:49:08] It is now inference in an agent to agentic system. [00:49:12] This is Vera Rubin. [00:49:15] No cables, no hoses, no fans. [00:49:21] What used to take the last time when I showed this to you, we had cables everywhere. [00:49:27] The cables were amazing to look at. [00:49:29] But now, there's a PCB in the middle, which connects both sides. [00:49:35] What used to take two hours, now takes five minutes. [00:49:39] The reliability and the resilience of Vera Rubin is going to be off the charts. [00:49:44] This is our Vera CPU tray. [00:49:48] The most advanced CPUs that has ever been built. [00:49:52] I'm going to show you that in just a second. [00:49:55] And this is our storage tray. [00:49:59] Two Vera CPUs, four CX9, incredible amounts of software. [00:50:07] This is our new LPX, LPU30, the GROC system, designed for very low latency inference. [00:50:17] The throughput is delivered by Vera Rubin and extended with NVLink 72. [00:50:23] If you want to extend that even further, you can have GROC LPUs. [00:50:29] Here, we have the Vera Rubin NVLink, the switch tray. [00:50:33] This is the switches in the middle, and this is revolutionary. [00:50:38] Because of Vera Rubin's, because of NVLink 72 and the NVLink switches that we created and invented. [00:50:46] And this is our Ethernet switches for scale out. [00:50:51] What's amazing is we introduced these two systems for Grace Blackwell. [00:50:58] These two systems were created for Grace Blackwell. [00:51:01] And today, NVIDIA is the largest networking company in the world. [00:51:07] I'm so proud of the networking team. [00:51:09] This is such an incredible enabler for everything that we do. [00:51:14] I'm going to now talk to you about the next major industry we're going to be part of. [00:51:20] Thank you. [00:51:22] I think there are 2,000 people back there pulling that. [00:51:38] Okay, let's talk about CPUs. [00:51:44] Varus CPUs, CPUs built for the age of AI. [00:51:53] All of the CPUs until now were created for people. [00:51:59] We were the users. [00:52:01] We were the users. [00:52:03] We were the renters. [00:52:05] The way we use CPUs, we live in a world counted by seconds. [00:52:12] The way we rent CPUs in the cloud, each one of them, the more CPU cores you have, the more you can rent. [00:52:20] The use case of the old CPU, and the economics of the old CPU, fundamentally different than agents. [00:52:29] Agents are impatient. [00:52:33] They don't live in a world that is in seconds. [00:52:35] They live in a world that's in nanoseconds. [00:52:38] When it uses a tool, it wants the response time to be as fast as possible. [00:52:45] When it accesses database, it has to come back as soon as possible. [00:52:50] Every moment that the agent is waiting, keeps it from going to the next step, the next step, the next step. [00:52:58] It is vital that we make the CPUs as low latency as possible, as interactive as possible. [00:53:07] So we created Vera CPU for the age of AI. [00:53:12] Now, inside our system, it's used for three different ways. [00:53:16] The first way, of course, is Vera Rubin for thinking. [00:53:23] And inside the Vera Rubin rack, there are already two CPUs. [00:53:28] As you know, we are building and selling millions of Vera Rubins. [00:53:35] In the world millions of Grace Blackwells. [00:53:38] NVIDIA already is one of the largest CPU makers in the world. [00:53:42] In the Vera Rubin rack are two CPUs. [00:53:46] One for orchestrating and managing the GPUs. [00:53:50] Managing the KV cache. [00:53:54] Dealing with all of the software that runs in the rack. [00:53:58] We also have the Grace Bluefield that is used for security and isolation. [00:54:04] The Vera Compute is used for the harness, the orchestration of the AI models, tool use, accessing the database. [00:54:14] And the data servers are right here, Vera Bluefield. [00:54:18] The fastest storage servers, the fastest storage system the world has ever made. [00:54:26] And the reason why this is so vital is because agents are accessing memory so incredibly fast. [00:54:34] These systems, the storage server and the CPUs are now the critical path of the most expensive part of the data center. [00:54:46] This is the most expensive for a good reason. [00:54:49] The economics, the economics of the AI factory is tokens. [00:54:56] And the tokens are created here. [00:54:59] And so of course you want to manufacture and generate as many tokens as possible. [00:55:04] This is where you put all of your economics and this has to not be in the way. [00:55:10] And so Vera CPU has great pressure on the Vera on the CPU architecture. [00:55:16] Which is the reason why we built a brand new architecture from the ground up. [00:55:21] A CPU the world has never seen before. [00:55:23] We call it Vera. [00:55:25] This is CPU for agents. [00:55:29] All the CPUs of the past we built for humans. [00:55:33] This CPU is built for agents. [00:55:36] Well, there are four things to keep in mind. [00:55:39] The four takeaways. [00:55:41] The first takeaway is that the instructions per clock of Vera has to be incredibly good. [00:55:49] Because we need the latency to be short. [00:55:51] We need the processing time. [00:55:53] Single threaded performance. [00:55:55] Not throughput. [00:55:57] Single threaded performance has to be world class. [00:56:00] Absolutely the best. [00:56:02] Single threaded performance. [00:56:03] Which is the reason why the IPC, the instructions per clock of Vera is so high. [00:56:09] It's the highest in the world. [00:56:11] Ten instructions fetched, decoded and executed per clock. [00:56:15] Number one. [00:56:17] Number two. [00:56:19] The bandwidth necessary to move data in and out for the CPU has to be utterly world class. [00:56:26] The second thing is bandwidth per core. [00:56:30] The third is just bandwidth period. [00:56:33] We're moving. [00:56:34] Remember, I said earlier, agentic systems is fundamentally disaggregated and distributed. [00:56:43] Disaggregated and distributed. [00:56:45] When computing is disaggregated and distributed, networking becomes the problem. [00:56:51] 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. [00:57:01] The bandwidth around the system and inside the CPU core has to be utterly world class. [00:57:08] This is the first CPU that's been built a long time that is literally at reticle limits with a fabric that connects all of the CPU cores that is speed of light. [00:57:21] 3.6 terabytes per second. [00:57:24] No chiplet tax, no chip boundary crossings because we need to have everything because the CPU cores are talking to each other with extremely high bandwidth. [00:57:36] They're not rented core per core per core. [00:57:39] They're all working together. [00:57:41] The cross-sectional bandwidth of Vera is off the charts. [00:57:45] The first one to be PCI Express Gen 6. [00:57:48] It is also the first one to have LPDDR5 with 1.2 terabytes per second. [00:57:56] Three times, two to three times the bandwidth of the highest performance CPUs on the outside. [00:58:03] Three times the bandwidth on the inside. [00:58:06] The bandwidth per core and the bandwidth period is world class. [00:58:11] Now, remember, I showed you earlier the number of CPU cores, the number of CPUs is going to be quite high. [00:58:19] And the reason for that is very simple. [00:58:22] We created CPUs for humans in the past and humans, there are only 1 billion of us. [00:58:31] There will be billions of agents and these agents are going to be using the CPUs with very little patience [00:58:40] because the cost of the GPUs they sit next to is too high. [00:58:45] And therefore, too valuable, too precious. [00:58:49] Therefore, these CPUs are going to be both performant, but they also have to be extremely energy efficient. [00:58:59] So 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. [00:59:10] 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. [00:59:25] This is absolutely world class. [00:59:28] When you compare it to the highest performance x86, it is just off the charts. [00:59:33] When you compare it in real single threaded performance, real performance, it's off the charts. [00:59:41] It is incredible to be able to deliver 5% improvement on CPUs. [00:59:46] It is incredible to be able to deliver 10%. [00:59:49] But this kind of performance speed up is just unheard of. [00:59:54] This is NVIDIA Vera. [00:59:56] What do you think? [01:00:05] Let's take a look. [01:00:07] Agentic AI changes the role of the CPU. [01:00:11] The CPU is now the conductor and the GPU is the orchestra. [01:00:15] Traditional CPUs were built for a different era, maximizing cores per socket, slice them up, virtualize, rent by the hour. [01:00:25] In the age of agents, the CPU is now a bottleneck to GPU utilization, directly affecting token throughput, latency, and user experience. [01:00:35] 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. [01:00:50] 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. [01:01:02] Each core is tuned for throughput, a neural branch predictor evaluating two taken branches per cycle. [01:01:09] A 10-wide decode engine brings in more work each cycle. [01:01:13] A large out-of-order engine keeps instructions moving. [01:01:16] Advanced prefetchers with a novel graph engine, anticipating the next data path. [01:01:21] But fast cores only matter when data arrives correctly and on time. [01:01:26] Vera is the first CPU to use LPDDR5X memory while correcting multiple errors simultaneously without compromising bandwidth. [01:01:36] Vera achieves 40% lower peak memory latency versus x86, keeping cores fed on time through retrieval, analytics, and sandbox execution. [01:01:47] NVIDIA's second-generation scalable coherency fabric unifies all 88 Olympus cores on a monolithic mesh with separate dies for memory and I/O. [01:01:58] Cores are not split across chiplets, enabling 50% faster core-to-core communication than traditional CPUs. [01:02:06] And memory coherent NVLink chip-to-chip connects GPUs directly to the fabric. [01:02:12] Beyond GPUs, NVLink chip-to-chip can scale Vera up to multiple sockets, enabling massive bandwidth between CPUs. [01:02:21] Vera delivers 1.8 times the agentic sandbox performance of x86 CPUs. [01:02:27] Standalone Vera racks run agent sandboxes, tools, code, and data pipelines. [01:02:33] Tightly coupled to Reuben GPUs, Vera keeps accelerated workflows moving. [01:02:39] NVIDIA Vera Bluefield 4 STX powers context memory and AI storage. [01:02:45] Compute, networking, storage. [01:02:49] Vera is the CPU for the age of agents. [01:02:57] You know, this is going to be our new major growth driver. [01:03:03] The reviews are already coming out and it's pretty good. [01:03:08] That's pretty good stuff. [01:03:10] Now remember, Grace and Vera are also the most highly qualified [01:03:26] CPUs in the world of AI because every single data center, [01:03:31] every single cloud, every single enterprise, [01:03:34] every company that works with NVIDIA on AI has already qualified Grace. [01:03:41] The entire software stack has already been optimized for Grace. [01:03:45] Every company will be qualifying Vera. [01:03:48] Vera will be the most optimized agentic CPU in the world. [01:03:53] simply because it's going to go with Vera Rubin, simply because we made the big heart switch. [01:04:00] In fact, during Grace Blackwell transition, the biggest risk was going from external CPU x86 into Grace Blackwell. [01:04:09] That transition was extremely dangerous, but we did it with incredible execution. [01:04:15] Now Grace is literally synonymous with Grace Blackwell. [01:04:20] When people say Blackwell, they say Grace Blackwell because it is utterly now everywhere. [01:04:25] Every company's software stack has been optimized for it. [01:04:28] Everybody's security stack has been optimized for it. [01:04:31] And now here comes Vera. [01:04:33] I'm super excited about that. [01:04:34] Now look at some of the performance numbers. [01:04:37] Speed up says one thing. [01:04:39] It is extremely hard to speed up SQL. [01:04:44] SQL, the most famous domain specific language DSL that has ever been created. [01:04:55] Before SQL, you know, before CUDA, there was SQL. [01:04:59] Before OpenGL, there was SQL. [01:05:02] Invented by IBM. [01:05:04] Today, it is the structured database engine of the planet. [01:05:08] Everybody uses SQL. [01:05:10] This is SQL running three times faster. [01:05:14] Not 10% faster. [01:05:15] Not 25% faster. [01:05:17] Not 25% faster. [01:05:18] 10 times faster. [01:05:19] Three times faster. [01:05:20] Incredible. [01:05:21] This is real-time. [01:05:24] The next one is real-time stream processing. [01:05:27] Remember, your AI is going to be not just reading documents. [01:05:32] Your AI is going to be watching for telemetry, especially inside a factory, inside a stock exchange. [01:05:40] You're going to be looking for telemetry continuously. [01:05:43] The burst of data that's coming in goes into a CPU. [01:05:48] This is Vera CPU running real-time stream processing for New York Stock Exchange. [01:05:54] Lynn Martin, the president of New York Stock Exchange, has been so gracious to partner with us. [01:06:00] This system is run all over the world in real-time stream processing. [01:06:05] Vera CPU six times. [01:06:07] It's all because of the bandwidth, the single-threaded instruction execution, the bandwidth inside between the cores, the bandwidth outside. [01:06:17] Vera is completely revolutionary. [01:06:20] That's Vera. [01:06:20] That's Vera. [01:06:21] You know, X-Factors is something you talk about when you're talking about GPUs. [01:06:32] It is quite rare that somebody talks about X-Factors on real workload, real workload that is associated with CPUs. [01:06:41] So I'm so proud of the team. [01:06:42] You guys did such a great job. [01:06:44] We have an extraordinary roadmap coming. [01:06:46] But what's really exciting is almost everybody is supporting Vera. [01:06:53] They're as excited as we are. [01:06:55] This is Vera opening up. [01:06:57] It's opened up. [01:06:58] It's opened up a brand new market. [01:07:02] Agents. [01:07:03] Agents is a new workload. [01:07:06] We built CPUs for humans in the past. [01:07:09] We need CPUs for agents, agentic systems. [01:07:13] The properties are different. [01:07:14] Why would the old CPUs be the same? [01:07:17] We are building millions and millions of errors. [01:07:22] Millions of errors. [01:07:23] And to go to market with us, Taiwan's ODMs and computer makers, all the OEMs, and you could see the early adopters. [01:07:34] The early adopters are the agentic companies. [01:07:38] This is the beginning of a new market, a market that never existed before. [01:07:43] It's not going to take away from the old markets, but this is a new market. [01:07:48] CPU for agents. [01:07:51] And this market will surely be larger than the last. [01:07:55] And the reason for that is because there'll be a lot more agents than there are people. [01:07:59] And then the agents are very impatient. [01:08:02] So, NVIDIA, Vera, CPU. [01:08:05] Thank you. [01:08:13] This is the most important slide, really. [01:08:15] This is the takeaway. [01:08:16] The takeaway here is that this is the application pattern. [01:08:21] This is the computing pattern of the next decade. [01:08:26] Agents, harnesses, orchestrating large language models. [01:08:33] Every company will run it. [01:08:35] Every company will be an agent company. [01:08:39] Every company will have agents running inside. [01:08:42] Every company will see that agents will need its own operating system. [01:08:49] Every company is asking us, how do we run agents safely? [01:08:53] How do we build agents for our own workloads? [01:08:57] And so we have the NVIDIA agent toolkit for enterprise AI. [01:09:04] You've seen me build this in plain sight. [01:09:07] Almost everything that NVIDIA does, as you know, at every GTC. [01:09:10] If you go back and look at my GTC five years ago or 10 years ago, you will see today. [01:09:15] This you've seen me talking about for several years now because we've been building for this moment. [01:09:23] There are four things that companies need in order to build agents as a service or build agents to operate. [01:09:32] The first thing you need is you need models. [01:09:35] Of course, large language models. [01:09:37] The smarter, the better. [01:09:39] The cheaper, the better. [01:09:40] The faster, the better. [01:09:42] The second is you need a harness to orchestrate the whole thing. [01:09:47] The third, these models want to use tools. [01:09:52] And these tools come with its skills. [01:09:54] And I showed you CUDAX libraries. [01:09:56] Those are going to be amazing tools for the agents in the future. [01:10:01] And then lastly, you need a runtime. [01:10:04] You need the operating system that holds it all together. [01:10:07] This is the NVIDIA toolkit for agents. [01:10:12] It includes models that you can modify. [01:10:19] NVIDIA's world-class open models, and I want to show you more. [01:10:22] You can run agents from anybody. [01:10:25] You could run Cloud Code, Incredible Agent. [01:10:29] Codex, Incredible Agent. [01:10:31] You could run it inside this harness called Open Shell, which will be highly secure for your inside the enterprise. [01:10:38] The shell protects the agent, keeps it grounded in security policies. [01:10:46] Privacy is protected. [01:10:48] Its rights and privileges are given. [01:10:51] Identities protected. [01:10:53] And so this Open Shell is being adopted all over the world. [01:10:56] NVIDIA Open Shell is open source. [01:10:59] You can see so many companies adopted. [01:11:01] Red Hat, Canonical, Microsoft. [01:11:05] It's going to be adopted everywhere. [01:11:07] This is an important, this is the runtime. [01:11:11] And this runtime is fully optimized for the NVIDIA AI platform, which is everywhere. [01:11:16] So you can run Open Shell in any cloud, on prem, and even on device. [01:11:23] So you have now tools and libraries that they can use. [01:11:29] You have models that you can modify or use as is. [01:11:33] Or you have agents. [01:11:35] This would be Open Claw, Hermes, another incredible, another incredible harness. [01:11:42] These agentic harnesses can now run on prem or for you anywhere. [01:11:48] Okay. [01:11:48] So four things. [01:11:50] And this represents the operating system of the modern enterprise. [01:11:54] Now, how do we use this? [01:11:57] One of my favorite use cases of agents is chip designers. [01:12:03] It is the single most important thing that NVIDIA does. [01:12:07] And so, of course, we have to partner with Cadence to build super agent. [01:12:13] A chip design super agent. [01:12:16] It is orchestrated by codex or cloud code. [01:12:21] It has RTL and architecture diagrams or schematics or specifications as input and whatever you need to fix. [01:12:30] And together we created some super agents that are optimized for the NVIDIA runtime with NemoTron. [01:12:40] And let's take a look. It's really incredible. [01:12:43] Cadence and NVIDIA are partnering to build chip design agents. [01:12:50] Hundreds of thousands of NVIDIA chips come together to make the AI factories that power the world's frontier AI models. [01:12:58] Designing these chips and the systems they run in is one of the hardest engineering challenges. [01:13:05] Trillions of transistors, three-dimensional circuits, microscopic scale. [01:13:10] Every gate, every wire, synchronized to picoseconds, must work in perfect harmony with no margin for error. [01:13:18] Physical prototypes are too slow and too costly. [01:13:21] So engineers work in the digital realm. [01:13:24] The chip begins as a set of architectural specifications, then translated into RTL, the language of chip design. [01:13:31] RTL must be verified in simulation. [01:13:34] A single bug can delay a chip by months. [01:13:37] At NVIDIA, thousands of engineers, billions of compute hours per year, millions of tests, written, run, and debugged. [01:13:45] A cycle that takes teams weeks. [01:13:47] To compress this cycle, Cadence and NVIDIA built a design verification agent. [01:13:52] Codex orchestrates the process. [01:13:54] Cadence ChipStack launches the RTL verification loop, powered by Nematron and secured by NVIDIA OpenShell, calling on expert sub-agents in RTL generation, test bench creation, regression testing, and debug. [01:14:10] The system drives itself. [01:14:12] The chip stack agents run hundreds of simulations with Cadence Exilium. [01:14:17] Formal verification with Jasper. [01:14:19] Design flaws, reveal. [01:14:21] Bugs in the code, fixed. [01:14:23] What once took weeks, now takes hours. [01:14:26] Verification cycles, over 40 times faster. [01:14:30] Together, NVIDIA and Cadence are reinventing chip design with AI agents. [01:14:35] From weeks, from weeks to hours, from weeks to hours, from weeks to hours, from weeks to hours, NVIDIA has thousands of chip designers. [01:14:47] We are going to hire hundreds of thousands of Cadence super agents that work with us so that we can accelerate our company, so that we can be even more ambitious, create even more amazing things, run even faster. [01:15:02] You saw earlier that the toolkit with models, harness, tools, the tools in this case are Cadence simulators and verifiers, formal verification systems. [01:15:16] It is the reason why we are working with Cadence so hard to accelerate all of their tools on CUDA, because the agents are impatient. [01:15:24] The agents want the answer immediately. [01:15:25] And so models, harnesses, accelerated CUDA, accelerated libraries and tools, and then the runtime. [01:15:37] What you saw just now is all of that coming together. [01:15:41] Now, one of the things that it starts with is a great model that Cadence could modify and tune to be expert at the Cadence workflow, at the Cadence expertise, [01:15:53] so that they could create super agents that are proprietary to Cadence with their proprietary knowledge. [01:16:01] They have to start with an excellent model, we call it NemoTron. [01:16:05] NVIDIA is dedicated to build open models for the world, so that all of you, all of us, could create our own agents. [01:16:14] Today, we are announcing the NemoTron 3 Ultra, our next open model, and it is smart. [01:16:26] The NemoTron models not only give you the model, we give you all the data that we use to train the model, and because we have a coalition of incredible partners, [01:16:43] you see all of our partners down here, we work together, contribute data to each other. [01:16:50] NemoTron is trained on one of the largest suites of long-running reasoning models, long-running tool, task-solving tool using data sets in the world. [01:17:02] Because of all of our great partnerships, all of this from the model, the training script, and the data made completely available to you. [01:17:13] This is open models at its best. [01:17:16] The best open model system policies in the world. [01:17:20] The simple goal is so that you can take all of it, add to it, make it even better, make it yours. [01:17:27] NemoTron 3 Ultra is five times faster. [01:17:32] This is the world's first model based on a hybrid architecture of SSM state-space models with mixture of experts. [01:17:43] The architecture is incredibly fast. [01:17:45] We made it fast so that you could think fast. [01:17:48] When you think fast, you could think longer at the same cost. [01:17:51] So five times faster. [01:17:52] It is also 30% cheaper, 30% lower cost to run in total flops and total inference time than even the most cost effective in the world. [01:18:06] We're comparing against the world's best open models. [01:18:09] Frontier smart. [01:18:10] Frontier smart. [01:18:12] Five times faster. [01:18:14] 30% cheaper. [01:18:16] Completely open. [01:18:18] We're completely dedicated to this. [01:18:20] This is now NemoTron 3. [01:18:22] We're currently working on NemoTron 4. [01:18:25] So this entire toolkit from models, harnesses, tools and skills, and runtimes is the reason why every enterprise company in the world has to do this. [01:18:39] The ability now to create their own agents, just like Cadence did with their super agents. [01:18:45] And we're working with so many companies, Cadence and CrowdStrike and DeSolo and Palantir, SAP and ServiceNow. [01:18:52] People always said, Jensen, the agents are going to disrupt these markets. [01:18:58] I said completely opposite and you can now see it. [01:19:02] Agents is going to create the largest opportunity ever for my partners and friends. [01:19:08] And we have the NEMO, the NVIDIA agentic toolkit for enterprise AI to help them. [01:19:15] So there you go. [01:19:19] First, Vera Rubin in full production. [01:19:26] Two, Vera CPU, CPU built for a new generation for agents. [01:19:32] And three, NVIDIA's enterprise AI toolkits so that every enterprise and every enterprise software company can build agents. [01:19:53] My relationship with you started here. [01:19:54] And many of you, many of you, many of my friends and partners here in Taiwan, your companies started here. [01:20:06] This is in a lot of ways, the beginning of the modern computer industry, 40 years now. [01:20:13] NVIDIA is 33 years old. [01:20:16] The PC industry was already starting to get to Windows 1 and Windows 2 and Apple, Apple 1 and Apple 2. [01:20:24] And by the time that we came along, Windows 3.1 was the PC. [01:20:30] And as you know, Windows 95 made PC personal. [01:20:35] It took PC from enterprises, companies and made it into a consumer electronics device. [01:20:43] Everybody should have one and everybody does. [01:20:46] This is the beginning. [01:20:48] This computing platform did several things incredibly smart. [01:20:52] Windows was not just disaggregated, as you know. [01:20:56] Windows was properly abstracted. [01:20:59] It was architected just right. [01:21:01] Systems BIOSs. [01:21:03] Open chip sets. [01:21:06] The operating system with drivers. [01:21:10] Drivers that could be connected and installed at runtime. [01:21:14] And an abstraction layer with a multimedia API that opened up the PC to what we all know today. [01:21:25] Each one of these elements were essential in making the PC so popular. [01:21:31] 40 years later, Microsoft and NVIDIA are going to reinvent the PC. [01:21:38] This is going to be the new PC. [01:21:42] Now tomorrow night, tomorrow night, I think it's tomorrow night, our time. [01:21:46] But I'm going to be with Satya. [01:21:48] We're going to talk a lot more about the work that we're doing together. [01:21:52] Microsoft and NVIDIA, over the last three years, it took this long to completely reinvent how the PC is going to work. [01:22:00] So that we could be ready for this moment. [01:22:03] As I mentioned earlier, that compute pattern called the agent is going to run in AI clouds. [01:22:11] It's going to run inside enterprises. [01:22:14] It is also going to run on your PC. [01:22:17] What's going to happen to that PC when it has an autonomous agent? [01:22:22] An agent that's helping you, that understands you. [01:22:25] You could talk to it. [01:22:26] It could look at you. [01:22:27] You could ask it to read files, go help you do some research. [01:22:34] It could do a lot more that I'll show you. [01:22:37] But the new operating system is, of course, the old operating system, plus large language models. [01:22:45] Large language models in a lot of ways is the modern version of DirectX. [01:22:51] It has, of course, input and output, understands prompts. [01:22:55] It understands computer vision. [01:22:56] It can generate video. [01:22:57] It can generate sounds. [01:22:59] What is the modern extension, the intelligence extension of the PC, of a computer? [01:23:06] On top of that, the application, as I mentioned before, is going to be replaced by now an agentic runtime. [01:23:15] And that is the modern application, an agent. [01:23:18] Let's now take a look at what it can do. [01:23:22] It started with a spark. [01:23:25] An idea. [01:23:27] To reimagine the PC for the first time in 40 years. [01:23:31] For the age of AI. [01:23:34] What becomes of our personal computer in a world of agents? [01:23:39] Agents running natively. [01:23:41] Connected to models. [01:23:42] Local or in the cloud. [01:23:44] Our personal AI. [01:23:46] Sandboxed for security. [01:23:48] Running continuously. [01:23:50] Getting work done. [01:23:52] The chips and the OS must evolve. [01:23:56] Introducing RTX Spark. [01:23:59] Everything we've learned over 33 years, distilled into one chip. [01:24:05] Blackwell RTX GPU with 6,144 CUDA cores. [01:24:11] One petaflop of AI performance. [01:24:14] A custom 20-core Grace CPU. [01:24:17] Built in partnership with MediaTek. [01:24:20] Fused by MVLink. [01:24:22] 128 gigabytes of unified memory. [01:24:26] TSMC 3 nanometer process. [01:24:29] 70 billion transistors. [01:24:33] And in close collaboration with Microsoft. [01:24:36] A Windows platform for agents. [01:24:39] We're reinventing the personal computer. [01:24:43] For creating. [01:24:46] For gaming. [01:24:49] For agents. [01:24:51] This is the dawn of a new personal computing revolution. [01:24:55] And it starts with NVIDIA RTX Spark. [01:24:58] Here it is. [01:24:59] Of course. [01:24:59] I got to show you the most beautiful part. [01:25:00] Which is video games. [01:25:01] It is also the closest to our heart. [01:25:05] This is Forza. [01:25:06] This is 007 by the way. [01:25:07] The new 007 game. [01:25:08] I'm looking forward to playing it. [01:25:08] I look a little bit like him. [01:25:08] Ladies and gentlemen. [01:25:09] NVIDIA's. [01:25:10] NVIDIA's. [01:25:11] NVIDIA's. [01:25:11] NVIDIA's. [01:25:12] RTX Spark. [01:25:13] Laptops. [01:25:14] Now. [01:25:15] Now. [01:25:16] . [01:25:17] Thank you. [01:25:18] NVIDIA. [01:25:19] Thank you. [01:25:19] NVIDIA. [01:25:20] it is it's also the closest to our heart this is Forza this is 007 by the way the new 007 game I'm [01:25:30] looking forward to playing it I look a little bit like him ladies and gentlemen NVIDIA's RTX spark [01:25:39] laptops now thank you I have too many things in my pocket title don't see okay all right this is the [01:25:59] most amazing trip the world has ever built this is the n1x that we built in partnership with media [01:26:07] tech I think I saw I saw Rick earlier this is n1x is a beautiful chip this is this is a a chip that [01:26:16] frankly would take 33 years to build and the reason for that is because a hundred percent of NVIDIA [01:26:23] software stack runs here if you want to run a digital biology no problem if you want to do [01:26:30] seismic processing no problem you want astrophysics no problem everything associated with CUDA all the [01:26:36] physics all the biology all the genomics all the AI no problem all the computer graphics no problem [01:26:42] every single application NVIDIA has ever created and every single application that Windows has ever run [01:26:52] Microsoft and NVIDIA meticulously optimized everything so that this computer literally runs [01:26:59] everything the world has ever created plus it now runs agents an incredible computer I'm so proud of it [01:27:09] okay now I want you to keep that in mind in the next video I just I'm going to show you just imagine [01:27:23] everything here is going to run on your PC now that computer could have a local Nemo Tron three ultra model [01:27:31] or Nemo Tron three supermodel or it could have a cloud code or codex or some other model in the cloud or [01:27:40] something on the network and it's going to work and do something amazing let's play it [01:27:46] every house starts as an idea getting from idea to design takes a myriad of tools expertise and a lot of time [01:27:57] 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 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 [01:28:09] my agent goes to work using the tools on my laptop it opens Rhino and starts modeling the site then it opens Rhino and starts modeling the site and the building envelope [01:28:27] then it proposes building forms optimized for cost comfort and quality with the form defined my agent generates the interior layout walls circulation rooms begin to take shape I jump in whenever I want to adjust to change [01:28:51] doors windows and structural elements are placed automatically my agent detects its own mistakes and fixes them [01:29:11] when I approve the agent exports the model from Rhino into blender materials and object properties transfer with the design context intact [01:29:20] I fine-tune the materials get the look just right then I pick the shots blender renders the house my agent using generative AI with the flux 2 model makes them photo real multiple viewpoints lighting conditions what was once a complex workflow is now guided and simplified by my agent working with on RTX Spark design at the speed of imagination [01:29:50] you can see in the world [01:29:55] he see in the world of agents the developers are so excited about this is an incredible computer all of the acceleration all the software capabilities associated with it working with every developer to make it incredible for all of you [01:30:09] for all of you the next one Adobe incredible tool suite of course used by tens of millions of people around the world they have re-engineered the architecture the core of Adobe Photoshop and Premiere and then release it for RTX Spark it is twice as fast as already fast now it's going to be twice as fast and it's also designed to be agent friendly with its MCP server it can now interact with agents on your laptop [01:30:38] the number of customers the number of partners that are so excited to bring RTX [01:30:43] are so excited to bring RTX RTX part to the market is just incredible you know this is the first across the lineup of PC reinvention for 40 years and I'm just so happy that all of you and the ecosystem around the world has joined us this is basically everybody everybody will support RTX Spark and will be building the right [01:31:07] and we'll be building incredibly smart and powerful and beautiful laptops with all of us thank you very much [01:31:14] but that's not all that's not all RTX Spark is a reinvention of laptop but in fact Microsoft NVIDIA is reinventing all of PC and today we're announcing a whole new line [01:31:36] three revolutionary Windows machines covering desktop laptop and workstations all 100% Windows compatible 100% CUDA 100% NVIDIA AI Tensor Core everything that runs that you see that runs on NVIDIA and all these different platforms around the world runs here [01:31:59] this is this is the first completely re-engineered re-engineered reinvented line of PCs [01:32:06] that has happened in 40 years now what's really amazing is this so this is this is the RTX Spark laptop this is the desktop so this one's from MSI [01:32:18] this one's from MSI Joseph this one's yours okay look how beautiful it is this agent could run 24/7 meter free and you could download your agent you could raise your lobster in here [01:32:33] this is your clock it's running all the time no meter anxiety and the senior connected to your whole house connected to your laptop connected to your display all the cameras your your dryer your water cooler your water heater your everything whatever you want your security system all connected to this and this becomes your personal AI [01:33:00] your personal AI your personal AI agent and it gets smarter and smarter and smarter over time because today we have nemotron 3 ultra tomorrow we have nemotron 4 and then nemotron 5 nemotron 6 and we just keep getting us smarter and smarter smarter and meanwhile this is sitting at home helping you do things if you want to book a travel no problem and if you if you want an incredible system this is a [01:33:05] DGX station for Windows. [01:33:12] compatible with Windows. [01:33:13] compatible with Windows. [01:33:13] compatible with Windows. [01:33:14] and everything in Windows. [01:33:15] and and it has 768 gigabytes of power. [01:33:16] and it has 768 gigabytes of power. [01:33:17] and it has 768 gigabytes of power. [01:33:18] and it has 768 gigabytes of power. [01:33:19] and it has 768 gigabytes of power. [01:33:20] and it has 768 gigabytes of power. [01:33:21] and it has 768 gigabytes of power. [01:33:22] and it has 768 gigabytes of power. [01:33:23] and it has 768 gigabytes of power. [01:33:25] and it has 768 gigabytes of power. [01:33:26] and it has 768 gigabytes of power. [01:33:29] and it has 768 gigabytes of power. [01:33:31] and it has 768 gigabytes of power. [01:33:33] and it has 768 gigabytes of power. [01:33:35] and it has 768 gigabytes of power. [01:33:37] and it has 768 gigabytes of power. [01:33:43] and so you could run a trillion parameter model. [01:33:46] this is unbelievable. [01:33:48] 20 petaflops. [01:33:49] 8 terabytes per second of memory bandwidth. [01:33:53] and this sits by your desk. [01:33:56] you basically if you're a developer of large language models. [01:34:00] you're a developer of agents. [01:34:03] having this sit by your desk. [01:34:05] it gives you all the compute you need. [01:34:07] and then when you deploy it. [01:34:08] you put it into the cloud. [01:34:09] Now. [01:34:10] there's something that. [01:34:12] if you look at this and think about this. [01:34:14] something is happening here. [01:34:17] remember. [01:34:20] 15, 20 years ago. [01:34:21] we used to have an idea called a phone. [01:34:25] today. [01:34:26] we have an idea called a PC. [01:34:29] today. [01:34:30] today. [01:34:31] when you think about your phone. [01:34:33] the one thing you don't do with it is make phone calls. [01:34:39] you do just about everything else. [01:34:41] and so that phone means something very different to you. [01:34:45] than a phone of the past. [01:34:47] I am certain. [01:34:50] what's going to happen here. [01:34:51] is that the PC. [01:34:52] 10 years from now. [01:34:53] and the PC that you think about today. [01:34:55] a tool. [01:34:56] whether you launch applications. [01:34:59] click and type. [01:35:02] and this PC. [01:35:03] is going to be completely different. [01:35:06] here's my theory. [01:35:08] I can totally imagine. [01:35:10] just as every house today has a. [01:35:12] home theater. [01:35:14] where many houses have home theaters. [01:35:15] big TVs. [01:35:18] lawnmowers. [01:35:20] dishwashers. [01:35:22] I could totally imagine that someday. [01:35:24] there's actually an AI supercomputer in your house. [01:35:28] and it's running all of your agents. [01:35:30] it's running all of your assistants. [01:35:32] and they're doing all kinds of things for you. [01:35:34] all the time. [01:35:36] and you have to have it in your house. [01:35:38] just like you have a home theater in your house. [01:35:40] you have stereos in your house. [01:35:41] you have game consoles in your house. [01:35:43] you want to assist AI agent computers running in your house. [01:35:48] and these. [01:35:50] in time. [01:35:51] becomes a lot more like. [01:35:53] R2D2 to you. [01:35:55] it becomes more like C3PO to you. [01:35:58] then it feels like a PC to you. [01:36:02] there is no question. [01:36:03] this reinvention of the computer. [01:36:05] is as big of a deal. [01:36:07] as the reinvention of the phone. [01:36:09] into what we now know as the smartphone. [01:36:12] and so this is the beginning of that journey. [01:36:14] this is the beginning of a new line. [01:36:17] and so we have a roadmap for this. [01:36:19] this is a brand new product family for us. [01:36:22] every single generation of architecture. [01:36:25] we will have a desktop. [01:36:27] a laptop. [01:36:28] a workstation. [01:36:29] and then a desktop. [01:36:30] a laptop. [01:36:31] and workstation. [01:36:33] and the thing that I am just incredibly pleased. [01:36:35] incredibly honored. [01:36:37] is that a hundred percent. [01:36:39] of the world's PC industry. [01:36:40] has joined us to reinvent the PC. [01:36:43] a new line. [01:36:44] a new beginning. [01:36:45] a new beginning. [01:36:46] thank you. [01:36:47] thank you. [01:37:01] as you know. [01:37:03] A Gentic AI. [01:37:04] is just a digital robot. [01:37:08] it understands. [01:37:10] it reasons. [01:37:11] it plans. [01:37:12] and it acts and use tools. [01:37:15] A Gentic AI is going to run across all of these computers. [01:37:20] and you've seen me talk about each and every one of these over time. [01:37:23] we're working on human or robotics computers. [01:37:26] robotics computers of all kinds. [01:37:28] we're working on self-driving car computers. [01:37:30] we're working on satellites. [01:37:33] you have g-force. [01:37:34] which has tensor cores. [01:37:35] i just talked about a whole new line of PCs. [01:37:39] agriculture equipment. [01:37:40] manufacturing equipment. [01:37:41] heavy industry equipment. [01:37:43] will all be a Gentic. [01:37:45] you'll even have a little a Gentic. [01:37:48] helper for yourself. [01:37:50] even your base stations. [01:37:51] the radio stations of the future. [01:37:53] are going to be a Gentic. [01:37:55] understanding traffic. [01:37:56] and thinking about. [01:37:58] how to coordinate with the other base stations. [01:38:01] so that you could use as little energy as possible. [01:38:04] increase. [01:38:06] the utilization. [01:38:07] the efficiency of the spectral efficiency. [01:38:10] and so everything will run agents. [01:38:13] today NVIDIA is largely in the center. [01:38:16] but I am pretty certain. [01:38:18] that there will be. [01:38:19] tens of billions. [01:38:20] hundreds of billions. [01:38:21] hundreds of billions. [01:38:22] over time. [01:38:23] of agentic systems. [01:38:24] agentic computers. [01:38:25] that are going to be running around the world. [01:38:28] the biggest problem. [01:38:29] is data. [01:38:31] in the case of language models. [01:38:33] all the English. [01:38:34] and all the language. [01:38:35] that we have on the internet. [01:38:36] that we trained on. [01:38:37] was from the perspective of us. [01:38:39] we wrote it. [01:38:40] and we're reading it. [01:38:42] however. [01:38:43] in order to create. [01:38:44] data for. [01:38:45] AI. [01:38:46] robotics. [01:38:47] it has to be in the perception. [01:38:49] the perspective. [01:38:50] of the robot. [01:38:51] and most. [01:38:52] of the world's video data. [01:38:54] is from a third person. [01:38:55] not first person. [01:38:56] first person. [01:38:57] and so. [01:38:58] agentic systems. [01:38:59] robotic systems. [01:39:00] physical AI. [01:39:02] the data is the hardest problem. [01:39:05] you've seen us. [01:39:06] move up this ladder. [01:39:07] we started with. [01:39:08] we started with. [01:39:09] tele operations. [01:39:10] which is basically. [01:39:11] human demonstration. [01:39:12] this is no different. [01:39:13] than the big breakthrough. [01:39:14] of reinforcement learning. [01:39:16] human feedback. [01:39:17] this. [01:39:18] then we use simulation. [01:39:19] this is where. [01:39:20] omniverse comes in. [01:39:21] this is no different. [01:39:22] than reinforcement learning. [01:39:24] verifiable rewards. [01:39:26] okay. [01:39:27] and so. [01:39:28] we use these systems. [01:39:29] to bootstrap. [01:39:32] the AI model. [01:39:33] the physical AI model. [01:39:34] eventually. [01:39:35] eventually. [01:39:36] we're able to learn. [01:39:38] from third. [01:39:39] third person. [01:39:40] reprojecting it. [01:39:41] into first person. [01:39:42] and now. [01:39:43] eventually. [01:39:44] through bootstrapping. [01:39:45] we have. [01:39:46] a world foundation model. [01:39:48] that can understand. [01:39:49] the physical world. [01:39:50] from any perspective. [01:39:51] you want. [01:39:52] third. [01:39:53] third person. [01:39:54] first person. [01:39:55] outside in. [01:39:56] inside out. [01:39:56] doesn't matter. [01:39:58] this. [01:39:59] is a big breakthrough. [01:40:00] indeed. [01:40:01] and today. [01:40:02] we're announcing. [01:40:04] third person. [01:40:05] cosmos three. [01:40:06] cosmos three. [01:40:07] cosmos three. [01:40:08] is the frontier. [01:40:10] of physical AI. [01:40:11] we are. [01:40:13] at the frontier. [01:40:14] with language models. [01:40:15] there's so many people. [01:40:16] working on it. [01:40:17] however. [01:40:18] in physical AI. [01:40:19] we are absolutely. [01:40:20] the world's best. [01:40:21] I am so proud. [01:40:22] of the team. [01:40:23] for doing this. [01:40:24] this is. [01:40:25] the foundation model. [01:40:26] for all of your work. [01:40:27] whenever you want. [01:40:28] whenever you want. [01:40:29] to create a robot. [01:40:30] whenever you want. [01:40:31] to create. [01:40:31] a factory robot. [01:40:32] or a robot. [01:40:32] that works. [01:40:33] in a factory. [01:40:34] any kind of robot. [01:40:35] there in. [01:40:36] that. [01:40:37] involves. [01:40:38] physical world. [01:40:39] you now have. [01:40:40] a companion. [01:40:41] a cosmos three. [01:40:42] that can. [01:40:43] understand. [01:40:44] and reason. [01:40:45] it can generate. [01:40:46] it can simulate. [01:40:47] it can simulate. [01:40:48] in the loop. [01:40:49] it can even be. [01:40:50] the policy itself. [01:40:51] itself. [01:40:52] it is on the top. [01:40:53] of leaderboards. [01:40:54] all over. [01:40:55] all over the world. [01:40:56] I am incredibly proud. [01:40:57] of cosmos. [01:40:58] and today. [01:40:59] we're announcing. [01:41:00] cosmos three. [01:41:01] let's take a look. [01:41:02] the real world. [01:41:03] is infinite. [01:41:04] and unpredictable. [01:41:05] physical AI. [01:41:06] needs data. [01:41:07] but real world data. [01:41:08] is impossible. [01:41:09] to scale. [01:41:10] for physical AI. [01:41:12] compute. [01:41:13] is data. [01:41:14] this. [01:41:15] is cosmos. [01:41:16] an open frontier. [01:41:17] omni model. [01:41:18] for physical AI. [01:41:19] built on a new. [01:41:20] mixture of. [01:41:21] transformers. [01:41:22] architecture. [01:41:23] pixels. [01:41:24] action. [01:41:25] sound. [01:41:26] and language. [01:41:27] flow into the. [01:41:28] autoregressive. [01:41:29] transformer. [01:41:30] which reasons. [01:41:31] plans. [01:41:32] and instructs. [01:41:33] the diffusion. [01:41:34] transformer. [01:41:35] which generates. [01:41:36] what comes next. [01:41:37] developers. [01:41:38] post train. [01:41:39] cosmos. [01:41:40] across. [01:41:41] embodiments. [01:41:42] and use cases. [01:41:43] as a vlm. [01:41:44] cosmos. [01:41:45] watches. [01:41:46] the physical world. [01:41:47] understands. [01:41:48] what's happening. [01:41:49] as a world model. [01:41:51] cosmos. [01:41:52] generates physics. [01:41:53] accurate. [01:41:54] synthetic video. [01:41:55] from an image. [01:41:56] text. [01:41:57] or video. [01:41:58] as a simulator. [01:41:59] cosmos. [01:42:00] closes the loop. [01:42:01] for policy training. [01:42:02] and evaluation. [01:42:03] and as the foundation. [01:42:04] of nvidia. [01:42:05] omnidreams. [01:42:06] an action. [01:42:07] conditioned. [01:42:08] world model. [01:42:09] cosmos. [01:42:10] predicts the future. [01:42:11] frame by frame. [01:42:12] post train. [01:42:13] post train. [01:42:14] cosmos. [01:42:15] and it becomes a world action model. [01:42:17] perceiving. [01:42:18] reasoning. [01:42:19] planning. [01:42:20] generating actions. [01:42:22] for robots of every kind. [01:42:25] for everything that moves. [01:42:28] a new kind of data. [01:42:31] a new kind of teacher. [01:42:33] generated by compute. [01:42:35] the future. [01:42:36] cosmos. [01:42:37] the foundation for developers of the age of physical AI. [01:42:42] It takes data plus compute. [01:42:57] gives you AI. [01:42:59] now that we have AI. [01:43:02] compute is data. [01:43:04] and so. [01:43:05] use cosmos three. [01:43:06] train a whole bunch of AI models. [01:43:08] cosmos is such an incredible open model system. [01:43:10] is exactly the same as nemotron. [01:43:12] we open the model. [01:43:13] we open the data. [01:43:14] and we even open. [01:43:15] how we trained it. [01:43:17] so that you could. [01:43:18] enhance it. [01:43:19] for yourself. [01:43:20] and turn cosmos. [01:43:21] into your proprietary model. [01:43:22] we have such incredible partners working with us. [01:43:25] in so many different industries. [01:43:26] now the model itself. [01:43:28] is that most of course. [01:43:30] the most understandable part. [01:43:32] of the AI stack. [01:43:33] but the AI stack is very complicated. [01:43:35] it has. [01:43:36] generators. [01:43:37] the model. [01:43:39] simulators. [01:43:41] and the runtime. [01:43:42] just as. [01:43:43] just as it is for agentic systems. [01:43:45] these cars. [01:43:46] or essentially a physical. [01:43:48] AI. [01:43:49] agentic. [01:43:50] robot. [01:43:51] that is a. [01:43:52] is an autonomous vehicle. [01:43:53] has also. [01:43:54] this complicated stack. [01:43:56] today. [01:43:57] we're announcing. [01:43:58] alpha mile 2. [01:43:59] an open model. [01:44:00] for self-driving cars. [01:44:02] driving cars. [01:44:03] we're working with. [01:44:04] car companies. [01:44:05] across the world. [01:44:06] if you look at. [01:44:07] these brands. [01:44:08] that have signed up. [01:44:09] for the NVIDIA Hyperion. [01:44:10] that are building. [01:44:11] NVIDIA Hyperion cars. [01:44:13] this represents. [01:44:14] about 80%. [01:44:16] of the world's. [01:44:18] cars. [01:44:19] the manufacturers represent. [01:44:20] 80% of the world's cars. [01:44:21] cars. [01:44:22] we are going to have. [01:44:23] a whole lot. [01:44:24] of NVIDIA Hyperion. [01:44:26] systems. [01:44:27] that are able. [01:44:28] to run alpha mile. [01:44:29] or anybody else's. [01:44:30] AV stack. [01:44:31] we are also. [01:44:32] connected into. [01:44:33] mobility services. [01:44:34] approximately. [01:44:35] 97% of the world's. [01:44:36] mobility services. [01:44:37] are connecting. [01:44:38] with us. [01:44:39] so that. [01:44:40] when we deploy. [01:44:41] alpha mile. [01:44:42] on. [01:44:43] the Hyperion. [01:44:44] runtime. [01:44:45] with the. [01:44:46] halos. [01:44:47] operating system. [01:44:48] we will be able. [01:44:49] to connect. [01:44:49] to all of these services. [01:44:50] across the world. [01:44:51] let's take a look at this. [01:44:55] hey mercedes. [01:44:56] let's go to my favorite. [01:44:57] sandwich shop. [01:44:59] routing to your destination. [01:45:03] lane is clear. [01:45:04] pulling out to start drive. [01:45:05] nudge left. [01:45:06] due to the stationary lead. [01:45:07] vehicle ahead. [01:45:08] blocking our lane. [01:45:09] slow down. [01:45:10] to stop at the stop sign. [01:45:11] controlling the intersection. [01:45:13] stop to yield to the pedestrian. [01:45:15] since the person is in our lane. [01:45:17] yield to the cut in vehicle. [01:45:18] from the left. [01:45:19] nudge left. [01:45:20] to clear the stopped vehicle. [01:45:21] blocking on the right. [01:45:22] keep distance to the cut in vehicle. [01:45:23] since it is merging into our lane. [01:45:25] nudge left. [01:45:26] due to the stopped van. [01:45:27] blocking the right side of our lane. [01:45:28] since the area in our lane. [01:45:29] pedestrian crossing ahead. [01:45:30] stop to keep distance. [01:45:31] to the lead vehicle. [01:45:32] to the vehicle directly ahead in our lane. [01:45:33] keep distance to the vehicle directly ahead in our lane. [01:45:35] stop to the stop sign. [01:45:36] since the intersection is controlled by the stop sign. [01:45:37] stop to yield to the cross traffic. [01:45:38] since the vehicle is crossing ahead. [01:45:39] keep distance to the lead vehicle. [01:45:40] nudge left due to the truck blocking on the right side of our lane. [01:45:41] nudge right due to the truck blocking on the left side of our lane. [01:45:42] nudge left due to the truck blocking on the right side of our lane. [01:45:43] nudge left due to the truck blocking on the right side of our lane. [01:45:44] nudge left due to the truck blocking on the right side of our lane. [01:45:45] nudge left due to the truck blocking on the right side of our lane. [01:45:46] nudge left due to the truck blocking on the right side of our lane. [01:45:47] nudge left due to the truck blocking on the right side of our lane. [01:45:48] nudge left due to the truck blocking on the right side of our lane. [01:45:49] nudge left due to the truck blocking on the right side of our lane. [01:45:50] nudge left due to the truck blocking on the right side of our lane. [01:45:51] nudge left due to the truck blocking on the right side of our lane. [01:45:52] nudge left due to the truck blocking on the right side of our lane. [01:45:53] nudge left due to the truck blocking on the right side of our lane. [01:45:54] nudge left due to the truck blocking on the right side of our lane. [01:45:55] nudge left due to the truck blocking on the right side of our lane. [01:45:57] nudge left due to the truck blocking on the right side of our lane. [01:46:02] nudge left due to the truck blocking on the right side of our lane. [01:46:03] nudge left due to the truck blocking on the right side of our lane. [01:46:04] nudge left due to the truck blocking on the right side of our lane. [01:46:05] nudge left due to the truck blocking on the right side of our lane. [01:46:06] nudge left due to the truck blocking on the right side of our lane. [01:46:07] nudge left due to the truck blocking on the right side of our lane. [01:46:08] nudge left due to the truck blocking on the right side of our lane. [01:46:09] nudge left due to the truck blocking on the right side of our lane. [01:46:10] nudge left due to the truck blocking on the right side of our lane. [01:46:11] nudge left due to the truck blocking on the right side of our lane. [01:46:12] nudge left due to the truck blocking on the right side of our lane. [01:46:13] nudge left due to the truck blocking on the right side of our lane. [01:46:14] nudge left due to the truck blocking on the right side of our lane. [01:46:15] nudge left due to the truck blocking on the right side of our lane. [01:46:16] nudge left due to the truck blocking on the right side of our lane. [01:46:17] nudge left due to the truck blocking on the right side of our lane. [01:46:18] nudge left due to the truck blocking on the right side of our lane. [01:46:19] nudge left due to the truck blocking on the right side of our lane. [01:46:20] nudge left due to the truck blocking on the right side of our lane. [01:46:23] nudge left due to the truck blocking on the right side of our lane. [01:46:25] nudge left due to the truck blocking on the right side of our lane. [01:46:26] nudge left due to the truck blocking on the right side of our lane. [01:46:27] nudge left due to the truck blocking on the right side of our lane. [01:46:28] nudge left due to the truck blocking on the right side of our lane. [01:46:29] nudge left due to the truck blocking on the right side of our lane. [01:46:30] nudge left due to the truck blocking on the right side of our lane. [01:46:31] nudge left due to the truck blocking on the right side of our lane. [01:46:32] nudge left due to the truck blocking on the right side of our lane. [01:46:33] nudge left due to the truck blocking on the right side of our lane. [01:46:34] nudge left due to the truck blocking on the right side of our lane. [01:46:35] nudge left due to the truck blocking on the right side of our lane. [01:46:38] nudge left due to the truck blocking on the right side of our lane. [01:46:40] nudge left due to the truck blocking on the right side of our lane. [01:46:41] nudge left due to the truck blocking on the right side of our lane. [01:46:42] nudge left due to the truck blocking on the right side of our lane. [01:46:43] nudge left due to the truck blocking on the right side of our lane. [01:46:44] nudge left due to the truck blocking on the right side of our lane. [01:46:45] nudge left due to the truck blocking on the right side of our lane. [01:46:46] nudge left due to the truck blocking on the right side of our lane. [01:46:47] nudge left due to the truck blocking on the right side of our lane. [01:46:48] nudge left due to the truck blocking on the right side of our lane. [01:46:49] nudge left due to the truck blocking on the right side of our lane. [01:46:50] nudge left due to the truck blocking on the right side of our lane. [01:46:53] nudge left due to the truck blocking on the right side of our lane. [01:46:55] nudge left due to the truck blocking on the right side of our lane. [01:46:56] nudge left due to the truck blocking on the right side of our lane. [01:46:57] nudge left due to the truck blocking on the right side of our lane. [01:46:58] nudge left due to the truck blocking on the right side of our lane. [01:46:59] nudge left due to the truck blocking on the right side of our lane. [01:47:00] nudge left due to the truck blocking on the right side of our lane. [01:47:01] nudge left due to the truck blocking on the right side of our lane. [01:47:02] nudge left due to the truck blocking on the right side of our lane. [01:47:03] nudge left due to the truck blocking on the right side of our lane. [01:47:04] nudge left due to the truck blocking on the right side of our lane. [01:47:05] nudge left due to the truck blocking on the right side of our lane. [01:47:09] nudge left due to the truck blocking on the right side of our lane. [01:47:10] nudge left due to the truck blocking on the right side of our lane. [01:47:11] nudge left due to the truck blocking on the right side of our lane. [01:47:12] nudge left due to the truck blocking on the right side of our lane. [01:47:13] nudge left due to the truck blocking on the right side of our lane. [01:47:14] nudge left due to the truck blocking on the right side of our lane. [01:47:15] nudge left due to the truck blocking on the right side of our lane. [01:47:16] nudge left due to the truck blocking on the right side of our lane. [01:47:17] nudge left due to the truck blocking on the right side of our lane. [01:47:18] nudge left due to the truck blocking on the right side of our lane. [01:47:19] nudge left due to the truck blocking on the right side of our lane. [01:47:20] nudge left due to the truck blocking on the right side of our lane. [01:47:23] nudge left due to the truck blocking on the right side of our lane. [01:47:25] nudge left due to the truck blocking on the right side of our lane. [01:47:26] nudge left due to the truck blocking on the right side of our lane. [01:47:27] nudge left due to the truck blocking on the right side of our lane. [01:47:28] nudge left due to the truck blocking on the right side of our lane. [01:47:29] nudge left due to the truck blocking on the right side of our lane. [01:47:30] nudge left due to the truck blocking on the right side of our lane. [01:47:31] nudge left due to the truck blocking on the right side of our lane. [01:47:32] nudge left due to the truck blocking on the right side of our lane. [01:47:33] nudge left due to the truck blocking on the right side of our lane. [01:47:34] nudge left due to the truck blocking on the right side of our lane. [01:47:35] nudge left due to the truck blocking on the right side of our lane. [01:47:38] nudge left due to the truck blocking on the right side of our lane. [01:47:40] nudge left due to the truck blocking on the right side of our lane. [01:47:41] nudge left due to the truck blocking on the right side of our lane. [01:47:42] nudge left due to the truck blocking on the right side of our lane. [01:47:43] nudge left due to the truck blocking on the right side of our lane. [01:47:44] nudge left due to the truck blocking on the right side of our lane. [01:47:45] nudge left due to the truck blocking on the right side of our lane. [01:47:46] nudge left due to the truck blocking on the right side of our lane. [01:47:47] nudge left due to the truck blocking on the right side of our lane. [01:47:48] nudge left due to the truck blocking on the right side of our lane. [01:47:49] nudge left due to the truck blocking on the right side of our lane. [01:47:50] nudge left due to the truck blocking on the right side of our lane. [01:47:53] nudge left due to the truck blocking on the right side of our lane. [01:47:55] nudge left due to the truck blocking on the right side of our lane. [01:47:56] nudge left due to the truck blocking on the right side of our lane. [01:47:57] nudge left due to the truck blocking on the right side of our lane. [01:47:58] nudge left due to the truck blocking on the right side of our lane. [01:47:59] nudge left due to the truck blocking on the right side of our lane. [01:48:00] nudge left due to the truck blocking on the right side of our lane. [01:48:01] nudge left due to the truck blocking on the right side of our lane. [01:48:02] nudge left due to the truck blocking on the right side of our lane. [01:48:03] nudge left due to the truck blocking on the right side of our lane. [01:48:04] nudge left due to the truck blocking on the right side of our lane. [01:48:05] nudge left due to the truck blocking on the right side of our lane. [01:48:08] nudge left due to the truck blocking on the right side of our lane. [01:48:09] nudge left due to the truck blocking on the right side of our lane. [01:48:10] nudge left due to the truck blocking on the right side of our lane. [01:48:11] nudge left due to the truck blocking on the right side of our lane. [01:48:12] nudge left due to the truck blocking on the right side of our lane. [01:48:13] nudge left due to the truck blocking on the right side of our lane. [01:48:14] nudge left due to the truck blocking on the right side of our lane. [01:48:15] nudge left due to the truck blocking on the right side of our lane. [01:48:16] nudge left due to the truck blocking on the right side of our lane. [01:48:17] nudge left due to the truck blocking on the right side of our lane. [01:48:18] nudge left due to the truck blocking on the right side of our lane. [01:48:20] nudge left due to the truck blocking on the right side of our lane. [01:48:23] nudge left due to the truck blocking on the right side of our lane. [01:48:26] nudge left due to the truck blocking on the right side of our lane. [01:48:27] nudge left due to the truck blocking on the right side of our lane. [01:48:28] nudge left due to the truck blocking on the right side of our lane. [01:48:29] nudge left due to the truck blocking on the right side of our lane. [01:48:30] nudge left due to the truck blocking on the right side of our lane. [01:48:31] nudge left due to the truck blocking on the right side of our lane. [01:48:32] nudge left due to the truck blocking on the right side of our lane. [01:48:33] nudge left due to the truck blocking on the right side of our lane. [01:48:34] nudge left due to the truck blocking on the right side of our lane. [01:48:35] nudge left due to the truck blocking on the right side of our lane. [01:48:36] nudge left due to the truck blocking on the right side of our lane. [01:48:39] nudge left due to the truck blocking on the right side of our lane. [01:48:41] nudge left due to the truck blocking on the right side of our lane. [01:48:42] nudge left due to the truck blocking on the right side of our lane. [01:48:43] nudge left due to the truck blocking on the right side of our lane. [01:48:44] nudge left due to the truck blocking on the right side of our lane. [01:48:45] nudge left due to the truck blocking on the right side of our lane. [01:48:46] nudge left due to the truck blocking on the right side of our lane. [01:48:47] nudge left due to the truck blocking on the right side of our lane. [01:48:48] nudge left due to the truck blocking on the right side of our lane. [01:48:49] nudge left due to the truck blocking on the right side of our lane. [01:48:50] nudge left due to the truck blocking on the right side of our lane. [01:48:51] nudge left due to the truck blocking on the right side of our lane. [01:48:53] nudge left due to the truck blocking on the right side of our lane. [01:48:54] nudge left due to the truck blocking on the right side of our lane. [01:48:56] nudge left due to the truck blocking on the right side of our lane. [01:48:57] nudge left due to the truck blocking on the right side of our lane. [01:48:58] nudge left due to the truck blocking on the right side of our lane. [01:48:59] nudge left due to the truck blocking on the right side of our lane. [01:49:00] nudge left due to the truck blocking on the right side of our lane. [01:49:01] nudge left due to the truck blocking on the right side of our lane. [01:49:02] nudge left due to the truck blocking on the right side of our lane. [01:49:03] nudge left due to the truck blocking on the right side of our lane. [01:49:04] nudge left due to the truck blocking on the right side of our lane. [01:49:06] nudge left due to the truck blocking on the right side of our lane. [01:49:08] nudge left due to the truck blocking on the right side of our lane. [01:49:09] nudge left due to the truck blocking on the right side of our lane. [01:49:10] nudge left due to the truck blocking on the right side of our lane. [01:49:11] nudge left due to the truck blocking on the right side of our lane. [01:49:12] nudge left due to the truck blocking on the right side of our lane. [01:49:13] nudge left due to the truck blocking on the right side of our lane. [01:49:14] nudge left due to the truck blocking on the right side of our lane. [01:49:15] computer. Fully pipe clean, ready to go in hours. First, set up the simulation environment in Isaac [01:49:22] Lab. Capture demonstrations with Isaac Teleop on a real or simulated robot. [01:49:34] Generate synthetic data with Omniverse and Cosmos, scaling one demonstration into thousands. [01:49:42] Train policies. Evaluate them in Isaac Lab Arena. Deploy through Isaac Ross, running on Jetson Thor. [01:50:03] Every element, modular, open, use hours or swap in your own. [01:50:10] Groot is powering robotics research across every discipline, for every domain, from research labs [01:50:17] to factory floors. One open platform. [01:50:27] And now, a new addition. Isaac Groot Reference Design Robots. Built on NVIDIA's open platform. Ready for [01:50:36] frontier research, for any lab, anywhere. The age of robotics starts here. NVIDIA Isaac Groot. [01:50:48] So many robots. [01:50:55] We're working with just about everybody who's working on robots in the world, or robotic systems in the world. [01:51:01] Let me tell you what I told you. The computer industry has been completely changed. [01:51:07] In the last six months, everything changed. Everything changed because agents were realized. [01:51:14] And it converged with the latest frontier models. And it made possible the AI to now do useful work. [01:51:22] The computing pattern will repeat over and over and over again. This computing pattern of an agent, [01:51:29] that's a model, a harness, that uses tools with skills, and runs in a runtime. That runtime depends [01:51:36] on whether it's in the cloud, or on-prem, on a PC, or in a robot. But the computing pattern is exactly the [01:51:42] same for all of them. You will use different harnesses because of your preference. You'll use different [01:51:48] models because of your preference. You will improve them for your proprietary use. You would create [01:51:53] sub-super agents that you can rent to other people to help them do their work. This agentic platform, [01:52:00] this agentic pattern, NVIDIA has an enterprise AI toolkit. This is a wonderful way for all of you to [01:52:08] engage AIs. And for us, it's a wonderful growth opportunity. Vera Rubin is in full production. [01:52:16] Whereas Grace Blackwell was created to process AI, particularly inference, Vera Rubin was created to [01:52:24] run agents. It is in full production. It is much, much more than a GPU. It is an entire disaggregated, [01:52:32] distributed agent processing system. NVIDIA has really become an infrastructure [01:52:37] company, not just a GPU company, not just a systems company, but an infrastructure company to help you [01:52:44] generate the maximum revenues, the maximum profit, and to get there as soon as possible. [01:52:50] The agent world, this new way of doing computing, where you build CPUs now for agents, not for people. [01:52:59] CPUs for agents has its own special requirement, and our NVIDIA Vera is revolutionary. I'm so happy about [01:53:06] it's RAMP. The orders already is going to make it the fastest and the most successful product launch [01:53:13] in our company's history. NVIDIA and Microsoft has created a whole new line of PCs. This is a new [01:53:19] beginning. And of course, that exact same agentic processing pattern, computing pattern that I just [01:53:26] described, is also going to run on all kinds of devices. I mentioned PCs. But in the future, it'll be robots and [01:53:36] satellites and base stations and factories in the cloud on-prem at the edge. This pattern, agentic AI [01:53:44] system, this agentic computing pattern will be replicated in computers all over. How we think about the [01:53:51] personal computer will very likely change. I want to thank all of you for your partnership, your [01:53:57] friendship. We couldn't be here without everything that we do together. I am so proud of how you've been so [01:54:03] successful this last year. The next year is going to be even more. I have one more thing for you. [01:54:10] Let's take a look. [01:54:18] The keynotes done at Computex. [01:54:27] The keynotes done at Computex. Jensen showed the world what's next. Useful AI has arrived, agents working by your side. But in case you missed things we said today, we're gonna break it all down for you, Taipei. [01:54:49] We're gonna break it all down for you, Taipei. Agents used to be misunderstood. Only movie stars had 'em in Hollywood. Now we all got teams making dreams come true. [01:55:00] Building companies from living rooms, but they need so much compute. We hear ya. That's why we created Vera. [01:55:07] Rubens, score, the show is true. The cheapest tokens coming through. Ten times faster, inference heaven. More special agents than 007. [01:55:16] Bluefield keeps agents' memory true. Now let's talk about its CPU. 50% faster, that's outrageous. Not for Vera. It's built for agents. [01:55:26] EnvyLink Fusion blends ASIC smartly. Everyone's welcome to the EnvyLink party. Well, if you like that introduction. Zero Rubens in full production. [01:55:37] MemoTron Ultra leads the run. 5x faster, work gets done. MemoClaw keeps the guardrails right. Open shell keeps the sandbox tight. Your code migrated and reviewed. All before this song is through. [01:55:52] ES is a five layer cake. Computes revenue. Make no mistake. Global AI clouds build lots of gigawatts. ESX keeps power lean. Connecting dots. [01:56:02] 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. [01:56:12] Agents powering our workflows. Running anywhere Windows goes. Harnesses run on CPU. Models fly on GPU. [01:56:21] Cosmos builds worlds that robots need. Turning compute into synthetic feed. Alpha Meiosis and reasons through. [01:56:29] Understands roads like people do. Who is how they learn to move. Learning skills and finding groove. Through the trees powered by thought. The future's humanoid. [01:56:41] Count on more! [01:57:11] The future's bright, come see what's next. [01:57:18] Thank you, Taiwan. [01:57:21] Welcome to Computex. [01:57:32] Have a great Computex. [01:57:37] Thanks for an amazing year. [01:57:39] Thank you for all your friendship and support. [01:57:42] Thank you. Take care. [01:57:44] Have a great Computex.