Matt Garman

The pitch for Tranium in both the training and inference use case is that it's a great deal.

You know, cost-effective, performant.

At the same time, you went on stage and said AWS is, quote, by far the best place to run NVIDIA GPUs.

How are both possible?

Well, I mean, both are possible because that is a great environment to run accelerators and compute in.

The plan for AWS is to basically double capacity by end of 2027 to around 8 gigawatts.

Do you have a sense of how you apportion that capacity in-house silicon and server designs of Tranium versus NVIDIA GPUs?

We're just going to keep pushing as fast as we can, and we'll see where customer demand drives us as we go.

And that's what we always listen to, and that's what we'll continue to listen to.

The focus with Tranium in the time I've been able to interact with you and talk about, again, not just the accelerator, but the server design level, there's a lot of benefits to the customer.

When does that benefit start accruing to AWS in terms of profitability?

Like if it's such a good financial proposition, you must be able soon to say we're making a lot of money on this.

Yeah, well, you're already seeing some of the benefits occur.

and our own products are going to get accelerated all from Tranium.

Every time you come onto the program, I always offer the audience opportunity to pose a question to you.

There's a lot of interest in AWS, right?

Many of your customers span global technology.

Actually, most of the questions were about Anthropic.

That wasn't much said on stage.

I think people are trying to understand what is the benefit and advantage AWS offers to Anthropic while they are ramping Tranium through Project Rainier.