Jensen Huang and Satya Nadella in Conversation at Microsoft Build

Thank you so much for being at Build again. I know it's late for you in Taipei. I really appreciate you staying up. You know, and then, you know, I've I've been looking at social and, you know, and everything people have been talking about since your keynote over the weekend. Uh, and suddenly, you know, this concept of unmetered intelligence right at the edge is so hot again. So, maybe you want to talk a little bit. You've thought about this, talked about this, and now, of course, with RTX Spark really delivered, I think, what's a breakthrough uh system for AI to be much more ubiquitous. But, maybe Jensen, you can just share a little bit your vision around where you see this going. >> Well, this all started about 3 years ago between a conversation between you and I. And we were talking about how we could build a new class of PCs that's incredible for designers and creators. And uh it would be incredible for artificial intelligence. And it would be one of these systems that uh has the processing capability, but also the software stack that's integrated into the world's design packages and creator packages. And And of course, all the things that we're doing with AI. And here we are uh 3 years later. We built an incredible new chip. And this system is supported by uh all of this new software that you created for Windows. And we now have the ability to have essentially an autonomous agent running on the PC. Now, when you take a step back and you think about what does that mean? For For the 40 years that or some 30 years we've been working together, uh we went from uh inventing DirectX together to uh creating uh now this uh incredible computer that has autonomous systems running. The PC evolved from being an incredible tool to now being a tool that's used autonomously by an AI assistant. And so, the idea that I could be traveling and I'm on the phone and I could text my PC and ask my PC to get some coding done or some idea that I have and it would fire up the tools on the PC and it would make the modifications or the changes or the design that I was I told it to to do and it would iterate with me while I'm away from the PC. My PC became an assistant. While I'm sitting there, of course, this PC would be my great assistant as well. And so, this idea that that the PC evolved from a from a personal computer to a personal AI is just really exciting. And to see it come to life, Satya, to see it come to life and actually doing that, you know, so I'm I'm super excited about it. I Spark, you you mentioned earlier, has all these incredible capabilities, a petaflop of AI performance. It has a petaflops of NVFP4, this numerical format that our two companies worked on together, that allows us to take advantage of this 128 GB of memory and fit maybe a couple of hundred billion parameter model. A couple hundred billion parameter model is state-of-the-art. And so, I think the days of having a really smart assistant running on the PC is here. >> Yeah, no, it's so awesome and in fact, I'm also excited about Windows coming to the GB300. Uh and so, that's another thing that it's kind of like data center right on your desktop and it's so exciting. But talking about that um data center side, obviously, you know, this entire thing got started when we built the first supercomputer uh together to train the GPT models and we've come a long way. In fact, even I was talking about the Fairwater design. It is custom-built essentially for the Grace Blackwell era to be able to max the the data center design with the system design you had. And now, of course, we're validating Vera Rubin. We're very excited about it. Maybe you want to share a little bit about sort of what happens even on the cloud side with how you're pushing on the systems innovation. >> Well, our journey's been incredible. We built the first AI supercomputer together. That was based on Ampere. Of course, Hopper was an incredible success. These first two generations were focused on pre-training. Grace Blackwell came along, and all of the focus moved to post-training, reinforcement learning, which allowed us to have reasoning models. And these reasoning models, based on mixture of experts, were incredibly intelligent, energy efficient, um but it requires giant systems. And so, we created NVLink 72, and the entire rack became one computer. We had evolved from one node to now one rack. Well, Microsoft deployed the largest number of Grace Blackwells in the world today. The fastest and the largest number of Grace Blackwells in the world. Fairwater is just a magnificent system to look at. It's just a miracle of engineering. It's It's just an incredible feat. Uh it's completely liquid cooled. You mentioned something earlier that I'm very proud of as well, that it's closed looped. Basically, uses almost no water, and it's incredibly environmentally friendly. It's energy efficient. We're able to increase the token generation rate and reduce the cost of token generation by an order of magnitude, some 30 times over Hopper. So, that was a huge achievement. Well, Vera Rubin was created for a world where these AIs are now agentic. And so, whereas Hopper was created for pre-training, Grace Blackwell for training, post-training, and also inference, Vera Rubin is designed to run agents. It's agents, as you know, this computing pattern is exactly the same computing pattern we're going to run on the RTX Spark. It's exactly the same agentic system, except of course, it's going to be much, much larger. We're going to process enormous number of them simultaneously. Many of them are going to be from different customers and different different partners. And so, the entire path, the entire coding path from storage, which is the long-term memory, the working memory, is encrypted. The data is encrypted in transit. The data is also encrypted in use. And so, we've We're going to really innovate in the area of confidential computing. And so, this entire disaggregated, distributed computing system, you mentioned CPUs, Vera is a revolutionary CPU designed for agents. You know, the past CPUs were designed for humans. And you know, we're just more patient than than agents are. And agents uh want low latency, just as have you been working on as well. Vera was designed for extremely low latency. And so, Vera Rubin is just completely revolutionary. I can't wait uh to show it to everybody. You've already stood it up. >> Yep. >> Our two teams Our two teams have been working very closely. And you know, almost long before the chips taped out, long before the systems were brought up, our two teams were already completely aligned. And so, the design the data centers were created for Vera Rubin, Vera Rubin is designed and integrated into your complete stack, into your networking, into your security. And so, the moment that our systems were rolling off the lines, they were being stood up at Microsoft and so incredibly excited about the collaboration. >> Yeah, I know this speed of light execution between the teams is fantastic to see. And of course, all this too is to power the ecosystem around us, right? I mean, you and I, having grown up with the PC, the server, and now with AI, have always thought about ultimately it's about creating the opportunity for every developer, every organization to build on the work that we do and the platforms we create. And speaking of that, there's a lot of software that NVIDIA builds that's all also coming. For example, we're going to have your models in foundry, your tooling in foundry. We're going to have, in fact, your software even help us with accelerating our workloads when it comes to even the data warehouse. We're going to obviously have stuff in Windows. Just talk a little bit about that broader vision of what does it mean for us an opportunity, right? Because everybody talks about this one model or one piece of tech. But it's about the broadest, biggest opportunity for people to create value. Maybe you want to share a little bit about that. >> Well, we've been preparing for this moment. You know, what happened in the last several months, we've been working for a decade and a half together getting ready for really what happened in the last several months. All of a sudden, because of agentic systems, the convergence of these really great models, AI is now useful. If you just look at GitHub, the the the commits into GitHub is gone completely parabolic. In the last several months, the the number of commits increased by factor of three. It's clear that agentic systems are useful, that it's doing productive work, and also tokens are now profitable as a result. And so, the amount of demand for compute between the the usage of the AI and the computation that's necessary for agents, the compute demand has really gone through the roof. Well, one of the things that we've been we been been doing together is making sure that all of the tools that the agents are going to use are fully accelerated. Fabric, for example, is now fully accelerated. We're accelerating data processing, um SQL, uh Spark, uh semantic-based, vector-based, uh graph-based. We're going to make sure that all of the tools that are available on Azure are going to be fully GPU accelerated because the agents are going to be impatient. The faster we can get the answers back to the agents, the faster they can iterate, the faster can generate tokens, which are ultimately what the developers, both of our customers, would like to do is generate a lot of tokens that are really profitable, that are highly intelligent. >> Now, thank you so much, Jensen, for the partnership and the leadership and the innovation uh that you bring to this entire ecosystem. And really thrilled to be working closely with you and the team and bring all this to the developers here and beyond. And look forward to seeing what the next sort of few months and the next year bring in terms of the innovation that gets built on top of the platform. So, thank you again for joining this late in the night uh from Taipei. >> Thank you so much, Satya, for your partnership and friendship. [applause] Thank you. >> Thank you.