Rene Haas

Because at that time, Jensen looked at what was going on with SOCs and Arm-based architecture and moved everybody on to the SOC program.

Yes.

Could not find a bidder.

That's right.

Oh boy, a lot there to describe.

So the way to think about NVIDIA, and to some extent, even though I'm the CEO of Arm, I don't want to tie it necessarily back to Arm, but in our world, what really drives demand is compute workloads.

At the end of the day, it's compute workloads.

And when a new workload is essentially either identified and or invented,

Then it comes down to what is the best architecture processor-wise to address that workload.

So let's look at AI, you know, the lightning bolt moment of AlexNet and the work actually that the Demison team were working on.

AI, particularly training, is a very, very complex parallel problem that is well-suited for a GPU.

And in fact, the very first work done by the engineers on AlexNet was not with Blackwell, it was not with an AI processor, but it was with a gaming GPU, a gaming card.

So NVIDIA was in a very, very good place to seize that moment relative to DeepMind moment slash AlexNet slash the transformer slash training and fast forward training these complex AI models as Demis was just talking about.

This is a huge, huge amount of work.

Now, what role does ARM play there?

Every one of these workloads requires a CPU to not only run the computer, but help the accelerator run.

And that's where NVIDIA is a customer today.

Their most advanced chip called Grace Blackwell is 72 ARM CPUs with a Blackwell architecture.

And that's where NVIDIA plays today.

Back to where does NVIDIA fit?