WEBVTT 00:00:01.430 --> 00:00:04.757 Thank you so much for being at Build again. 00:00:04.840 --> 00:00:07.157 I know it's late for you in Taipei. 00:00:07.240 --> 00:00:09.510 I really appreciate you staying up. 00:00:10.440 --> 00:00:17.167 You know, at the end, I've been looking at social and you know, and everything 00:00:17.250 --> 00:00:20.567 people have been talking about since your keynote over the weekend. 00:00:20.650 --> 00:00:26.107 And suddenly, you know, this concept of unmetered intelligence 00:00:26.190 --> 00:00:29.447 right at the edge is so hot again. 00:00:29.530 --> 00:00:31.217 So maybe you want to talk a little bit. 00:00:31.300 --> 00:00:33.217 You've thought about this, talked about this. 00:00:33.300 --> 00:00:37.287 And now of course with RTX Spark really delivered, 00:00:37.370 --> 00:00:42.257 I think what's a breakthrough system for AI to be much more ubiquitous. 00:00:42.340 --> 00:00:46.097 But maybe Jensen, you can just share a little bit your vision around where 00:00:46.180 --> 00:00:47.310 you see this going. 00:00:48.520 --> 00:00:50.837 Well, this all started about three years ago 00:00:50.920 --> 00:00:55.107 between a conversation between you and I, and we were talking about how 00:00:55.190 --> 00:00:58.207 we could build a new class of PCs. 00:00:58.290 --> 00:01:02.717 That's incredible for designers and creators, and it would be incredible 00:01:02.800 --> 00:01:06.087 for artificial intelligence. And it would be one of these systems 00:01:06.170 --> 00:01:11.117 that has the processing capability, but also the software stack 00:01:11.200 --> 00:01:15.127 that's integrated into the world's design packages and creator packages 00:01:15.210 --> 00:01:17.627 and of course, all the things that we're doing with AI. 00:01:17.710 --> 00:01:23.167 And here we are three years later, we built an incredible new chip. 00:01:23.250 --> 00:01:29.107 And this system is supported by all of this new software 00:01:29.190 --> 00:01:33.677 that you created for Windows. And we now have the ability to have essentially 00:01:33.760 --> 00:01:37.530 an autonomous agent running on the PC. 00:01:37.800 --> 00:01:41.347 Now, when you take a step back and you think about what does that mean? 00:01:41.430 --> 00:01:45.557 For the 40 years, that or some 30 years we've been working together, 00:01:45.640 --> 00:01:52.557 we went from inventing DirectX together to creating now 00:01:52.640 --> 00:01:56.437 this incredible computer that has autonomous systems running. 00:01:56.520 --> 00:02:01.667 The PC evolved from being an incredible tool 00:02:01.750 --> 00:02:06.677 to now being a tool that's used autonomously by an AI assistant. 00:02:06.760 --> 00:02:11.747 And so the idea that I could be traveling and I'm on the phone 00:02:11.830 --> 00:02:16.757 and I could text my PC and ask my PC to get some coding done 00:02:16.840 --> 00:02:21.970 or some idea that I have, and it would fire up the tools on the PC, 00:02:22.380 --> 00:02:26.597 and it would make the modifications or the changes or the design that I was, 00:02:26.680 --> 00:02:32.197 I told it to to do, and it would iterate with me while I'm away from the PC. 00:02:32.280 --> 00:02:35.807 My PC became an assistant. While I'm sitting there, of course, 00:02:35.890 --> 00:02:38.137 this PC would be my great assistant as well. 00:02:38.220 --> 00:02:44.117 And so this idea that that the PC evolved from a personal computer 00:02:44.200 --> 00:02:47.547 to a personal AI, it's just really exciting. 00:02:47.630 --> 00:02:52.887 And to see it come to life, Satya, to see it come to life and actually doing that, 00:02:52.970 --> 00:02:54.727 you know, so I'm super excited about it. 00:02:54.810 --> 00:02:59.597 Spark, as you mentioned earlier, has all this incredible capabilities, 00:02:59.680 --> 00:03:05.367 a petaflop of AI performance. It has a petaflop of NVFP4, 00:03:05.450 --> 00:03:09.007 this numerical format that our two companies worked on together. 00:03:09.090 --> 00:03:15.047 That allows us to take advantage of this 128GB of memory and fit 00:03:15.130 --> 00:03:18.577 maybe a couple of hundred billion parameter model. 00:03:18.660 --> 00:03:21.917 A couple of hundred billion parameter model is state of the art. 00:03:22.000 --> 00:03:25.957 And so I think the days of having a really smart assistant running 00:03:26.040 --> 00:03:27.727 autonomously on the PC is here. 00:03:27.810 --> 00:03:31.097 Yeah, I know it's so awesome. And in fact, I'm also excited about Windows 00:03:31.180 --> 00:03:36.567 coming to the GB300. And so that's another thing that it's kind of like data center 00:03:36.650 --> 00:03:38.797 right on your desktop. And it's so exciting. 00:03:38.880 --> 00:03:45.037 But talking about that data center side, obviously, you know, this entire thing 00:03:45.120 --> 00:03:48.447 got started when we built the first supercomputer 00:03:48.530 --> 00:03:51.377 together to train the GPT models. 00:03:51.460 --> 00:03:56.117 And we have come a long way. In fact, even I was talking about the Fairwater design. 00:03:56.200 --> 00:04:02.527 It was custom built essentially for the Grace Blackwell era to be able to max the 00:04:02.610 --> 00:04:05.997 the data center design with the system design you had. 00:04:06.080 --> 00:04:08.267 And now, of course, we're validating Vera Rubin. 00:04:08.350 --> 00:04:11.497 We're very excited about it. Maybe you want to share a little bit about sort of 00:04:11.580 --> 00:04:15.337 what happens, even on the cloud side, with how you're pushing 00:04:15.420 --> 00:04:20.147 on the systems innovation. Well, our journey is incredible. 00:04:20.230 --> 00:04:24.917 We built the first AI supercomputer together that was based on Ampere. 00:04:25.000 --> 00:04:27.317 Of course, Hopper was an incredible success. 00:04:27.400 --> 00:04:30.487 The first two generations were focused on pre-training. 00:04:30.570 --> 00:04:36.997 Grace Blackwell came along and all of the focus moved to post training, 00:04:37.080 --> 00:04:41.167 reinforcement learning, which allowed us to have reasoning models. 00:04:41.250 --> 00:04:44.297 And these reasoning models, based on mixture of experts, 00:04:44.380 --> 00:04:50.577 were incredibly intelligent, energy efficient, but it requires giant systems. 00:04:50.660 --> 00:04:56.447 And so we created NVLink72 and the entire rack became one computer. 00:04:56.530 --> 00:04:59.887 We had evolved from one node to now one rack. 00:04:59.970 --> 00:05:00.803 Well, 00:05:02.240 --> 00:05:07.287 Microsoft deployed the largest number of Grace Blackwells in the world 00:05:07.370 --> 00:05:11.657 today, the fastest and the largest number of Grace Blackwells in the world. 00:05:11.740 --> 00:05:15.927 Fairwater is just a magnificent system to look at. 00:05:16.010 --> 00:05:18.197 It's just a miracle of engineering. 00:05:18.280 --> 00:05:22.167 It's just an incredible feat. It's completely liquid cooled. 00:05:22.250 --> 00:05:25.037 You mentioned something earlier that I'm very proud of as well, 00:05:25.120 --> 00:05:28.917 that it's closed looped, basically uses almost no water, 00:05:29.000 --> 00:05:33.487 and it's incredibly environmentally friendly. It's energy efficient. 00:05:33.570 --> 00:05:37.717 We're able to increase the token generation rate and reduce 00:05:37.800 --> 00:05:43.697 the cost of token generation by an order of magnitude, some 30 times over Hopper. 00:05:43.780 --> 00:05:45.597 So that was a huge achievement. 00:05:45.680 --> 00:05:52.467 Well, Vera Rubin was created for a world where these AIs are now agentic. 00:05:52.550 --> 00:05:55.907 And so where as Hopper was created for pretraining, Grace 00:05:55.990 --> 00:06:00.047 Blackwell for training, post training and also inference. 00:06:00.130 --> 00:06:03.417 Vera Rubin is designed to run agents. 00:06:03.500 --> 00:06:06.917 It's agents. As you know, this computing pattern 00:06:07.000 --> 00:06:11.687 is exactly the same computing pattern we're going to run on the RTX Spark. 00:06:11.770 --> 00:06:15.727 It's exactly the same agentic system, except of course, it's going to be much, 00:06:15.810 --> 00:06:20.427 much larger when process enormous number of them simultaneously. 00:06:20.510 --> 00:06:23.167 Many of them are going to be from different customers 00:06:23.250 --> 00:06:24.697 and different different partners. 00:06:24.780 --> 00:06:31.107 And so the entire path, the entire coding path from storage, which is the long 00:06:31.190 --> 00:06:34.877 term memory, the working memory is encrypted, 00:06:34.960 --> 00:06:38.047 the data is encrypted in transit. 00:06:38.130 --> 00:06:40.147 The data is also encrypted in use. 00:06:40.230 --> 00:06:42.717 And so we've we're going to really innovate 00:06:42.800 --> 00:06:44.717 in the area of confidential computing. 00:06:44.800 --> 00:06:49.097 And so this entire disaggregated distributed computing system 00:06:49.180 --> 00:06:53.997 you mentioned CPUs. Vera is a revolutionary CPU designed for agents. 00:06:54.080 --> 00:06:56.437 You know, the past CPUs were designed for humans. 00:06:56.520 --> 00:06:59.707 And, you know, we're just more patient than than agents are. 00:06:59.790 --> 00:07:04.977 And agents want low latency, just as if you've been working on as well. 00:07:05.060 --> 00:07:07.447 And Vera is designed for extremely low latency. 00:07:07.530 --> 00:07:10.447 And so Vera Rubin is just completely revolutionary. 00:07:10.530 --> 00:07:12.877 I can't wait to show it to everybody. 00:07:12.960 --> 00:07:14.687 You've already stood it up. Yep. 00:07:14.770 --> 00:07:18.017 Our two teams, our two teams have been working very closely. 00:07:18.100 --> 00:07:23.757 And, you know, almost long before the chips taped out, long before 00:07:23.840 --> 00:07:27.227 the systems were brought up, our two teams were already completely aligned. 00:07:27.310 --> 00:07:31.067 And so the design, the data centers were created for Vera Rubin. 00:07:31.150 --> 00:07:34.507 The Vera Rubin is designed and integrated into your complete stack, 00:07:34.590 --> 00:07:36.577 into your networking into your security. 00:07:36.660 --> 00:07:40.507 And so the moment that our systems were rolling off the lines, 00:07:40.590 --> 00:07:42.607 they were being stood up at Microsoft. 00:07:42.690 --> 00:07:45.247 So I'm incredibly excited about the collaboration. 00:07:45.330 --> 00:07:49.317 Yeah, I know this speed of light execution between the teams is fantastic to see. 00:07:49.400 --> 00:07:53.957 And of course, all this is to power the ecosystem around us, right? 00:07:54.040 --> 00:07:58.697 I mean, you and I, having grown up with the PC, the server and now with AI, 00:07:58.780 --> 00:08:02.627 have always thought about ultimately it's about creating the opportunity 00:08:02.710 --> 00:08:06.467 for every developer, every organization to build on the work that we do 00:08:06.550 --> 00:08:10.737 and the platforms we create. In speaking of that, there's a lot of software 00:08:10.820 --> 00:08:14.147 that Nvidia builds. That's all also coming. 00:08:14.230 --> 00:08:17.917 For example, we're going to have your models in Foundry your tooling in Foundry. 00:08:18.000 --> 00:08:20.917 We're going to have, in fact, your software even help us 00:08:21.000 --> 00:08:25.657 with accelerating our workloads when it comes to even the data warehouse. 00:08:25.740 --> 00:08:28.627 We're going to obviously have stuff in Windows. 00:08:28.710 --> 00:08:33.627 Just talk a little bit about that broader vision of what does it mean for us, 00:08:33.710 --> 00:08:36.097 an opportunity, right. Because everybody talks about this 00:08:36.180 --> 00:08:42.007 one model or one piece of tech, but it's about the broadest, biggest opportunity 00:08:42.090 --> 00:08:47.107 for people to create value. Maybe you want to share a little bit about that. 00:08:47.190 --> 00:08:49.207 Well, we've been preparing for this moment. 00:08:49.290 --> 00:08:51.577 You know what happened in the last several months. 00:08:51.660 --> 00:08:56.287 We've been working for a decade and a half together, getting ready for really 00:08:56.370 --> 00:08:57.787 what happened in the last several months. 00:08:57.870 --> 00:09:01.827 All of a sudden, because of agentic systems, the convergence of these 00:09:01.910 --> 00:09:05.257 really great models, AI is now useful. 00:09:05.340 --> 00:09:12.267 If you just look at GitHub, the the commits into GitHub is gone 00:09:12.350 --> 00:09:17.237 completely parabolic. In the last several months, the number of commits 00:09:17.320 --> 00:09:19.437 increased by a factor of three. 00:09:19.520 --> 00:09:22.617 It's clear that agentic systems are useful, 00:09:22.700 --> 00:09:28.647 that it's doing productive work, and also tokens are now profitable as a result. 00:09:28.730 --> 00:09:31.617 And so the amount of demand for compute 00:09:31.700 --> 00:09:37.627 between the usage of the AI and the computation that's necessary for agents, 00:09:37.710 --> 00:09:40.127 the compute demand has really gone through the roof. 00:09:40.210 --> 00:09:44.497 Well, one of the things that we've been we've been doing together is making sure 00:09:44.580 --> 00:09:48.907 that all of the tools that the agents are going to use are fully accelerated. 00:09:48.990 --> 00:09:51.937 Fabric, for example, is now fully accelerated. 00:09:52.020 --> 00:09:57.477 We're accelerating data processing, SQL, Spark, 00:09:57.560 --> 00:10:02.047 semantic based vector, vector based, graph based. 00:10:02.130 --> 00:10:05.417 We're going to make sure that all of the tools that are available on Azure 00:10:05.500 --> 00:10:08.587 are going to be fully GPU accelerated, because the agents 00:10:08.670 --> 00:10:11.427 are going to be impatient. The faster we can get the answers 00:10:11.510 --> 00:10:15.767 back to the agents, the faster they can iterate, the faster can generate tokens, 00:10:15.850 --> 00:10:19.837 which are ultimately what the developers both of our customers would like 00:10:19.920 --> 00:10:22.837 to do is generate a lot of tokens that are really profitable, 00:10:22.920 --> 00:10:25.077 that are highly intelligent. Now. 00:10:25.160 --> 00:10:28.647 Thank you so much, Jensen, for the partnership and the leadership 00:10:28.730 --> 00:10:33.717 and the innovation that you bring to this entire ecosystem, and really thrilled 00:10:33.800 --> 00:10:38.187 to be working closely with you and the team and bring all this to the developers 00:10:38.270 --> 00:10:42.927 here and beyond, and look forward to seeing what the next few months 00:10:43.010 --> 00:10:45.627 and the next year bring in terms of the innovation 00:10:45.710 --> 00:10:48.097 that gets built on top of the platform. 00:10:48.180 --> 00:10:52.237 So thank you again for joining this late in the night from Taipei. 00:10:52.320 --> 00:10:55.837 Thank you so much Satya for your partnership and friendship. Thank you. 00:10:55.920 --> 00:10:56.753 Thank you.