Grant Harvey

So if you're ready to go from prompt to production faster, check it out at warp.dev.

That's really, really interesting.

And one of the things that's impressed me a lot is, like, I run 9B.

And...

you know, while, while yes, you know, there are some minor sacrifices, it's really not as much as you would think compared to a 600 trillion, 600 billion model, billion parameter model, excuse me, a 600 billion parameter model.

But it's, uh, it's, it's amazing to me that the difference isn't,

massive, at least in the tasks I'm doing with it specifically.

So how do you make sure that smaller and faster doesn't mean less accurate?

Is that that second phase of retraining you were talking about?

That's awesome.

That's a totally fair answer.

That was a good answer.

And kind of figured that would be the case, but it's really interesting the way you've explained it.

When should teams recognize that it's time to lean in to one of the bigger models versus nano?

Like, you know, what kind of signs are they looking for when that happens?

That's right.

We did a call with someone talking about parakeet just a couple of days ago and how they use it for podcast transcripting and things like that and how great it is.

So Carrie, tell me, if you don't mind, what has it been like working for NVIDIA through this wild transition over the last few years of growing to essentially the biggest company in the world?

We have a, we both regularly talk about, we have a lot of respect for Jensen as well.

And just kind of the, the foresight it took to sort of understand what was coming and be so beautifully positioned just, you know, right in time to become the cornerstone of modern AI, essentially.