Jensen Huang

And meanwhile,

There's so many new use cases that they want to put out into the world, and it's currently limited by the capacity they have.

And so this is a really important time.

You're seeing an exponential growth in the amount of compute demand necessary for AI.

You're seeing an exponential growth of adoption and use of AI.

And the number of applications that are going to be using these AI is also growing.

And so we've got to do our best to support the scaling out of two of the most consequential companies in history.

And we're delighted to be partnered with them.

NVIDIA's architect, NVIDIA is unlike any other accelerator.

And the reason for that is because of CUDA's

Diversity of capability and versatility.

Remember, I said two things earlier.

I said the fact that NVIDIA participates and could accelerate every phase of AI, pre-training, post-training, and inference.

We're the only architecture in the world that does that fantastically.

The second thing we do is we run every single model.

And so most agentic systems, most clouds, are running so many different diverse type of models, language models, vision models, biological models, chemical models, for all the different fields of science.

NVIDIA could be used across the entire lifespan of the technology.

And so if you look at products that we shipped, Ampere A100, we shipped six years ago.

But because we're continuing, our install base is so large, our diversity is so great,

We could continuously update our software, bring value to our customers on the one hand, but because our versatility is so great,