Andrew Feldman

The hard part here, the hard part is moving data from memory to compute.

The hard part here, the hard part is moving data from memory to compute.

This is the fundamental problem in AI.

This is the fundamental problem in AI.

And we solved it with a way that very few others had even attempted, which was to build a very big chip and to put memory right next to compute.

And we solved it with a way that very few others had even attempted, which was to build a very big chip and to put memory right next to compute.

By building a big chip, a chip the size of a dinner plate, whereas most chips are the size of a postage stamp, we could use a different type of memory.

By building a big chip, a chip the size of a dinner plate, whereas most chips are the size of a postage stamp, we could use a different type of memory.

And by using a different type of memory, a memory that was vastly faster, we opened up all sorts of opportunity.

And by using a different type of memory, a memory that was vastly faster, we opened up all sorts of opportunity.

So when open AI uses us, we're 15 or 18 times faster than a GPU.

So when open AI uses us, we're 15 or 18 times faster than a GPU.

That means your answers are delivered more quickly.

That means your answers are delivered more quickly.

It means your engagement with the AI is more enjoyable.

It means your engagement with the AI is more enjoyable.

It means you can use the AI to solve harder problems and not wait.

It means you can use the AI to solve harder problems and not wait.

And the way to think about this is sort of to ask yourself the counterfactual question.

And the way to think about this is sort of to ask yourself the counterfactual question.