Carmen Li

So that's where we come in.

The way I encourage them to do is I selfishly, I want them to start trading that on compute.

The more people trade, the better for me, right, selfishly.

But at the same time, it's important for people to understand GPU trading, it's not like you can't just move someone's phone, trade all your electricity with no background context, drop into GPU, compute futures.

There's a lot of contexts where, number one, GPU, it is not a homogeneous product.

Number two, you have to understand the use cases for A100, H100.

Right now, they are not that correlated.

Is that right?

Maybe that's not right.

I don't know.

There are use cases which are pretty separated.

But maybe there are use cases they can be transferred.

And also there's a software layer to this, right?

So right now, you can argue certain use cases, some large models cannot be deployed.

the legacy chips, but doesn't mean six months later, you cannot do so.

As the software layer compression, model compression gets better, optimization gets better, things can change.

So really understand not just the hardware configuration, this local supply demand curve for the

server itself, also the software layer.

That's kind of critical, right?

That's really changed the supply-demand curve and all the way to the user behavior.