Caroline Hyde

Yes.

How?

What are you doing to remove the technical economic barriers that you say exist when deploying GPUs?

Yeah, absolutely.

So what we have is we have this inference engine that is a proprietary engine that can run image and video models up to three to four times faster.

And we do that by optimizing NVIDIA chips to run these workloads.

So this is a proprietary technology that we built in-house.

It's software that you've built?

It is software, that's right.

You know, that's the interesting debate in the world we're in.

If you look at some of the ASICs and custom silicon solutions, the concern is they don't have the library of software to go with it.

We're here to talk about FOL, but would you just reflect on the reality of needing to use NVIDIA for that reason?

But also, you've built your own software.

Absolutely.

So we could run our workloads on ASICs, but the scale at which we operate is a lot larger, and there's no real ASIC out there that has the scale, that's one.

And then the second point is that one of our key value props is being day zero.

And if you have a model and you want to run that model whenever it's available, the first day it's available, you actually have to have really good software that's already ready to deploy.

With ASICs, typically what you have to do is actually customizing the software a lot before you can actually launch it.

So with NVIDIA, the software stack is so much more mature, so we can actually do these day zero releases.

So this is one of the key reasons why we prefer NVIDIA right now.