You have arrived at a developer's conference.

There will be a lot of science described, algorithms, computer architecture, mathematics.

Blackwell is not a chip.

Blackwell is the name of a platform.

People think we make GPUs, and we do.

But GPUs don't look the way they used to.

This is Hopper.

Hopper changed the world.

This is Blackwell.

It's OK, Hopper.

208 billion transistors.

And so you could see, I can see, there's a small line between two dies.

This is the first time two dies have been abutted like this together in such a way that the two dies think it's one chip.

There's 10 terabytes of data between it, 10 terabytes per second, so that these two sides of the Blackwell chip have no clue which side they're on.

There's no memory locality issues, no cache issues.

It's just one giant chip, and it goes into two types of systems.

The first one is form-fit function compatible to Hopper.

And so you slide on Hopper, and you push in Blackwell.

That's the reason why one of the challenges of ramping is going to be so efficient.

There are installations of Hoppers all over the world, and they could be, you know, the same infrastructure, same design.

The power, the electricity, the thermals, the software, identical, push it right back.

And so this is a Hopper version for the current HGX configuration.

And this is what the second Hopper looks like this.

Now, this is a prototype board.

This is a fully functioning board, and I'll just be careful here.

This right here is, I don't know, $10 billion?

The second one's $5 billion.

It gets cheaper after that, so any customers in the audience, it's okay.

The Gray CPU has a super fast chip-to-chip link.

What's amazing is this computer is the first of its kind where this much computation, first of all, fits into this small of a place.

Second, it's memory coherent.

They feel like they're just one big happy family working on one application together.

We created a processor for the generative AI era, and one of the most important parts of it is content token generation.

We call it, this format is FP4.

The rate at which we're advancing computing is insane, and it's still not fast enough, so we built another chip.

This chip is just an incredible chip.

We call it the NVLink Switch.

It's 50 billion transistors.

It's almost the size of Hopper all by itself.

This switch chip has four NVLinks in it, each 1.8 terabytes per second, and it has computation in it, as I mentioned.

What is this chip for?

If we were to build such a chip, we can have every single GPU talk to every other GPU at full speed at the same time.

You can build a system that looks like this.

Now, this system is kind of insane.

This is one DGX.

This is what a DGX looks like now.

Just so you know, there are only a couple, two, three Exaflops machines on the planet as we speak, and so this is an Exaflops AI system in one single rack.

I want to thank some partners that are joining us in this.

AWS is gearing up for Blackwell.

They're going to build the first GPU with secure AI.

They're building out a 222 Exaflops system for CUDA-accelerating SageMaker AI, for CUDA-accelerating Bedrock AI.

Amazon Robotics is working with us using NVIDIA Omniverse and Isaac Sim.

AWS Health has NVIDIA Health integrated into it, so AWS has really leaned into accelerated computing.

Google is gearing up for Blackwell.

GCP already has A100s, H100s, T4s, L4s, a whole fleet of NVIDIA CUDA GPUs, and they recently announced the GEMMA model that runs across all of it.

We're working to optimize and accelerate every aspect of GCP.

We're accelerating Dataproc, which is for data processing, their data processing engine, JAX, XLA, Vertex AI, and MuJoCo for robotics, so we're working with Google and GCP across a whole bunch of initiatives.

Oracle is gearing up for Blackwell.

Oracle is a great partner of ours for NVIDIA DGX Cloud, and we're also working together to accelerate something that's really important to a lot of companies, Oracle Database.

Microsoft is accelerating, and Microsoft is gearing up for Blackwell.

Microsoft and NVIDIA has a wide-ranging partnership.

We're accelerating CUDA-accelerating all kinds of services when you chat, obviously, and AI services that are in Microsoft Azure.

It's very, very likely NVIDIA is in the back doing the inference and the token generation.

They built the largest NVIDIA InfiniBand supercomputer, basically a digital twin of ours or a physical twin of ours.

We're bringing the NVIDIA ecosystem to Azure, NVIDIA DGX Cloud to Azure, NVIDIA Omniverse is now hosted in Azure, NVIDIA Healthcare is in Azure, and all of it is deeply integrated and deeply connected with Microsoft Fabric.

And now, it's a pre-trained model, so it's pretty clever, and it is packaged and optimized to run across NVIDIA's installed base, which is very, very large.

What's inside it is incredible.

You have all these pre-trained, state-of-the-art open-source models.

They could be open-source.

They could be from one of our partners.

It could be created by us, like NVIDIA Moment.

It is packaged up with all of its dependencies.

So CUDA, the right version, CUDNN, the right version, TensorRT, LLM, distributing across the multiple GPUs, Trident inference server, all completely packaged together.

It's optimized, depending on whether you have a single GPU, multi-GPU, or multi-node of GPUs, it's optimized for that, and it's connected up with APIs that are simple to use.

These packages, incredible bodies of software, will be optimized and packaged, and we'll put it on a website, and you can download it, you can take it with you, you can run it in any cloud, you can run it in your own data center, you can run it in workstations if it fit, and all you have to do is come to ai.nvidia.com.

We call it NVIDIA Inference Microservice, but inside the company, we all call it NIMS.

We have a service called NIMO Microservice that helps you curate the data, preparing the data so that you could teach this, onboard this AI, you fine-tune them, and then you guardrail it, you can even evaluate the answer, evaluate its performance against other examples.

And so we are effectively an AI foundry.

We will do for you and the industry on AI what TSMC does for us building chips.

And so we go to it with our, go to TSMC with our big ideas, they manufacture it, and we take it with us.

And so exactly the same thing here, AI foundry, and the three pillars are the NIMS, NIMO Microservice, and DGX Cloud.

We're announcing that NVIDIA AI foundry is working with some of the world's great companies.

SAP generates 87% of the world's global commerce.

Basically, the world runs on SAP.

We run on SAP.

NVIDIA and SAP are building SAP Jewel co-pilots using NVIDIA NIMO and DGX Cloud.

ServiceNow, they run, 85% of the world's Fortune 500 companies run their people and customer service operations on ServiceNow.

And they're using NVIDIA AI foundry to build ServiceNow assist virtual assistants.

Cohesity backs up the world's data.

They're sitting on a goldmine of data.

Hundreds of exabytes of data, over 10,000 companies.

NVIDIA AI foundry is working with them, helping them build their Gaia generative AI agent.

Snowflake is a company that stores the world's digital warehouse in the cloud and serves over 3 billion queries a day for 10,000 enterprise customers.

Snowflake is working with NVIDIA AI foundry to build co-pilots with NVIDIA NIMO and NIMS.

NetApp, nearly half of the files in the world are stored on-prem on NetApp.

NVIDIA AI foundry is helping them build chatbots and co-pilots like those vector databases and retrievers with NVIDIA NIMO and NIMS.

And we have a great partnership with Dell.

Everybody who is building these chatbots and generative AI, when you're ready to run it, you're going to need an AI factory.

And nobody is better at building end-to-end systems of very large scale for the enterprise than Dell.

And so anybody, any company, every company will need to build AI factories.

And it turns out that Michael is here.

He's happy to take your order.

We need a simulation engine that represents the world digitally for the robot so that the robot has a gym to go learn how to be a robot.

We call that virtual world Omniverse.

And the computer that runs Omniverse is called OVX.

And OVX, the computer itself, is hosted in the Azure cloud.

The future of heavy industries starts as a digital twin.

The AI agents helping robots, workers and infrastructure navigate unpredictable events in complex industrial spaces will be built and evaluated first in sophisticated digital twins.

Once you connect everything together, it's insane how much productivity you can get.

And it's just really, really wonderful.

All of a sudden, everybody's operating on the same ground truth.

You don't have to exchange data and convert data, make mistakes.

Everybody is working on the same ground truth.

From the design department to the art department, the architecture department, all the way to the engineering and even the marketing department.

Today, we're announcing that Omniverse cloud streams to the Vision Pro.

And it is very, very strange that you walk around virtual doors when I was getting out of that car.

And everybody does it.

It is really, really quite amazing.

Vision Pro, connected to Omniverse, portals you into Omniverse.

And because all of these CAD tools and all these different design tools are now integrated and connected to Omniverse, you can have this type of workflow.

Really incredible.

This is NVIDIA Project Grid.

A general purpose foundation model for humanoid robot learning.

The group model takes multimodal instructions and past interactions as input and produces the next action for the robot to execute.

We developed Isaac Lab, a robot learning application to train Grid on Omniverse Isaac Sim.

And we scale out with Osmo, a new compute orchestration service that coordinates workflows across DGX systems for training and OVX systems for simulation.

The group model will enable a robot to learn from a handful of human demonstrations so it can help with everyday tasks.

And emulate human movement just by observing us.

All this incredible intelligence is powered by the new Jetson Thor robotics chips designed for Groot, built for the future.

With Isaac Lab, Osmo and Groot, we're providing the building blocks for the next generation of AI powered robotics.

About the same size.

The soul of NVIDIA.

The intersection of computer graphics, physics, artificial intelligence, it all came to bear at this moment.

The name of that project, General Robotics 003.

I know, super good.

Super good.

Super good.

Well, I think we have some special guests.

Do we?

Hey guys.

So I understand you guys are powered by Jetson.

They're powered by Jetsons.

Little Jetson robotics computers inside.

They learned to walk in Isaac Sim.

Ladies and gentlemen, this is Orange.

And this is the famous Green.

They are the BDX robots of Disney.

Amazing Disney research.

Come on you guys, let's wrap up.

Let's go.

Five things.

Where are you going?

I sit right here.

Don't be afraid.

Come here, Green.

Hurry up.

What are you saying?

No, it's not time to eat.

It's not time to eat.

I'll give you a snack in a moment.

Let me finish up real quick.

Come on, Green.

Hurry up.

Stop wasting time.

This is what we announced to you today.

This is Blackwell.

This is the platform.

Amazing processors.

NVLink switches.

Networking systems.

And the system design is a miracle.

This is Blackwell.

And this to me is what a GPU looks like in my mind.