Welcome to GTC! What an amazing year. We wanted to do this at NVIDIA. So through the magic of artificial intelligence, we're going to bring you to NVIDIA's headquarters. I think I'm bringing you to NVIDIA's headquarters.

What do you think? This is where we work. This is where we work. What an amazing year it was. And we have a lot of incredible things to talk about. And I just want you to know that I'm up here without a net. There are no scripts. There's no teleprompter. And I've got a lot of things to cover. So let's get started. GTC started with GeForce. It all started with GeForce.

And today, I have here a GeForce 5090. And 5090, unbelievably, 25 years later, 25 years after we started working on GeForce, GeForce is sold out all over the world. This is the 5090, the Blackwell generation. And comparing it to the 4090, look how it's 30% smaller in volume. It's 30%.

Better at dissipating energy and incredible performance. Hard to even compare, and the reason for that is because of artificial intelligence. GeForce brought CUDA to the world. CUDA enabled AI, and AI has now come back to revolutionize computer graphics. What you're looking at is real-time computer graphics, 100% path traced,

For every pixel that's rendered, artificial intelligence predicts the other 15.

Think about this for a second. For every pixel that we mathematically rendered, artificial intelligence inferred the other 15. And it has to do so with so much precision that the image looks right and it's temporally accurate, meaning that from frame to frame to frame, going forward or backwards because it's computer graphics, it has to stay temporally stable. Incredible. Today, I'm super excited to announce

GM has selected NVIDIA to partner with them to build their future self-driving car fleet. The time for autonomous vehicles has arrived, and we're looking forward to building with GM AI in all three areas.

AI for manufacturing, so they can revolutionize the way they manufacture. AI for enterprise, so they can revolutionize the way they work, design cars and simulate cars, and then also AI for in the car. So AI infrastructure for GM, partnering with GM, and building with GM their AI. So I'm super excited about that. One of the areas that I'm deeply proud of, and it rarely gets any attention, is safety, automotive safety.

It's called halos. In our company, it's called halos. Safety requires technology from silicon to systems, the system software, the algorithms, the methodologies, everything from diversity to ensuring diversity, monitoring and transparency,

Explainability. All of these different philosophies have to be deeply ingrained into every single part of how you develop the system and the software. We're the first company in the world, I believe, to have every line of code safety assessed. Seven million lines of code safety assessed. Our chip, our system, our system software, and our algorithms are safety

Assessed by third parties that crawl through every line of code to ensure that it is designed to ensure diversity, transparency, and explainability. Alright, let's talk about data centers. That's not bad, huh?

It's in full production, and this is what it looks like. It's an incredible, incredible... You know, for people, for us, this is a sight of beauty. Would you agree? This is... How is this not beautiful? How is this not beautiful? Well, this is a big deal because we made a fundamental transition

In computer architecture. And so the thing that we had to do was scale up first. Well, this is the way we scaled up. I'm not going to lift this. This is 70 pounds. This is the last generation system architecture. It's called HGX. This revolutionized computing as we know it. This revolutionized artificial intelligence. This is eight GPUs.

Eight GPUs, each one of them is kind of like this. This is two GPUs, two Blackwell GPUs in one Blackwell package. Two Blackwell GPUs in one Blackwell package. And there are eight of these underneath this. And this connects into what we call MVLink 8. This then connects to a CPU shelf

Like that. So there's dual CPUs, and that sits on top. And we connect it over PCI Express, and then many of these get connected with InfiniBand, which turns into what is an AI supercomputer. This is the way it was in the past. We need to disaggregate the NVLink system and take it out. So this is the NVLink system. This is an NVLink switch. This is the highest performance switch the world's ever made.

And this makes it possible for every GPU to talk to every GPU at exactly the same time at full bandwidth. Okay, so this is the NVLink switch. We disaggregated it, we took it out, and we put it in the center of the chassis. So there's all the, there are 18 of these switches in nine different racks, nine different switch trays, we call them.

And then the switches are disaggregated, the compute is now sitting in here. This is equivalent to these two things in compute. What's amazing is this is completely liquid cooled, and by liquid cooling it, we can compress all of these compute nodes into one rack.

This is the big change of the entire industry. All of you in the audience, I know how many of you are here, I want to thank you for making this fundamental shift from integrated MVLink to disaggregated MVLink, from air-cooled to liquid-cooled, from 60,000 components per computer or so,

To 600,000 components per rack, 120 kilowatts, fully liquid cooled, and as a result, we have a one ExaFlops computer in one rack. Isn't it incredible? The way to solve this problem is to disaggregate it, as I described, into

The Grace Blackwell MV-Link 72 rack. But as a result, we have done the ultimate scale-up. This is the most extreme scale-up the world has ever done. The amount of computation that's possible here, the memory bandwidth, 570 terabytes per second. Everything in this machine is now in Ts. Everything's a trillion.

And you have an Exaflops, which is a million trillion floating-point operations per second. Today we're announcing the NVIDIA Dynamo. NVIDIA Dynamo does all that. It is essentially the operating system of an AI factory.

Whereas in the past, in the way that we ran data centers, our operating system would be something like VMware. And we would orchestrate, and we still do, you know, we're a big user, we would orchestrate a whole bunch of different enterprise applications running on top of our enterprise IT. But in the future, the application is not enterprise IT, it's agents. And the operating system is not something like VMware, it's something like Dynamo.

And this operating system is running on top of not a data center, but on top of an AI factory. Now, we call it Dynamo for a good reason. As you know, the Dynamo was the first instrument that started the last industrial revolution, the industrial revolution of energy.

Water comes in, electricity comes out. It's pretty fantastic. Water comes in, you light it on fire, turn it into steam, and what comes out is this invisible thing that's incredibly valuable. It took another 80 years to go to alternating current, but Dynamo. Dynamo is where it all started. So we decided to call this operating system, this piece of software, insanely complicated software, the NVIDIA Dynamo. This is what a PC should look like. 20 petaflops.

Unbelievable, 72 CPU cores, chip-to-chip interface, HBM memory, and just in case, some PCI Express slots for your GeForce.

So this is called DGX Station. DGX Spark and DGX Station are going to be available by all of the OEMs. HP, Dell, Lenovo, Asus. It's going to be manufactured for data scientists and researchers all over the world. This is the computer of the age of AI.

This is what computers should look like, and this is what computers will run in the future. And we have a whole lineup for enterprise now, from little tiny one to workstation ones, to server ones, to supercomputer ones, and these will be available by all of our partners. So let's go talk about robotics, shall we? Let's talk about robots. Well, the time has come, the time has come for robots.

Robots have the benefit of being able to interact with the physical world and do things that otherwise digital information cannot. We know very clearly that the world has severe shortage of human laborers, human workers. By the end of this decade, the world is going to be at least 50 million workers short.

We'd be more than delighted to pay them each $50,000 to come to work. We're probably going to have to pay robots $50,000 a year to come to work. And so this is going to be a very, very large industry. We created a system called Omniverse. It's our operating system for physical AIs. You've heard me talk about Omniverse for a long time. We added two technologies to it. Today I'm going to show you two things. One of them

So that we could scale AI with generative capabilities and generative model that understand the physical world. We call it Cosmos. Using Omniverse to condition Cosmos and using Cosmos to generate an infinite number of environments allows us to create data that is grounded, controlled by us,

And yet be systematically infinite at the same time. So you see Omniverse, we used candy colors to give you an example of us controlling the robot in the scenario perfectly, and yet Cosmos can create all these virtual environments. The second thing, just as we were talking about earlier, one of the incredible scaling capabilities of language models today is reinforcement learning,

Verifiable rewards. The question is, what's the verifiable rewards in robotics? And as we know very well, it's the laws of physics. Verifiable physics rewards. And so we need an incredible physics engine.

Well, most physics engines have been designed for a variety of reasons. They could be designed because we want to use it for large machineries or maybe we design it for virtual worlds, video games and such. But we need a physics engine that is designed for very fine-grained, rigid and soft bodies, designed for being able to train tactile feedback

Fine motor skills and actuator controls. We needed to be GPU accelerated so that these virtual worlds could live in super linear time, super real time, and train these AI models incredibly fast. And we needed to be integrated harmoniously into a framework that is used by roboticists all over the world, MuJoCo. And so today we're announcing

Something really, really special. It is a partnership of three companies, DeepMind, Disney Research, and Nvidia, and we call it Newton. Let's take a look at Newton. Tell me that wasn't amazing.

Hey, Blue. How are you doing? How do you like your new physics engine? You like it, huh? Yeah, I bet. I know. Tactile feedback, rigid body, soft body simulation, super real-time. Can you imagine just now what you were looking at as complete real-time simulation? This is how we're going to train robots in the future?

Just so you know, Blue has two computers, two NVIDIA computers inside. Look how smart you are. Yes, you're smart. Okay. All right. Hey, Blue, listen. How about let's take them home? Let's finish this keynote. Our robotics has been making enormous progress. And today we're announcing that Groot N1

It's open sourced. Well, have a great GTC. Thank you. Hey, blue. Let's go home. Good job.