Jaeden Schaefer

This is very purpose-built.

It's a silicon platform, and they are aimed at one of the most expensive and also it's one of the most complex parts of modern AI systems if you're looking at this from kind of like an operational perspective, and that is large-scale inference.

The Maya 200 is the successor of the Maya 100, which Microsoft, they actually launched that one back in 2023 as it was kind of like their first serious in-house AI chip that they were creating.

This new generation, now that they've made the 200, is a really big step forward.

So there's a couple of things that it does.

Number one is just raw performance.

And then also how tightly the chip is integrated into Microsoft's kind of broader cloud and also AI stack.

So according to them, the Maya 200 has more than 100 billion transistors and it's capable of delivering up to 10 petaflops of performance in a 4-bit precision and roughly 5 petaflops in 8-bit, which is a massive increase over the last generation.

And I think it's really trying to optimize for just running larger language models efficiently and doing this in production.

It's interesting to me seeing Microsoft get into the chips game.

There's a lot of competitors in this space, but not a lot of competitors that could really compete at this level.

And Microsoft, I think, sees just how much money they'll have to spend, let alone, you know, not to mention just how they're not able to customize everything the way they like if they're if they're using outside suppliers for this.

So it's interesting for me seeing them get into this.

And for those that are curious, right, inference is essentially just the process of executing a training AI model to generate outputs as opposed to training, which involves teaching the model in the first place.

Right.

So we have inference, which is getting it to generate for you.

And what's interesting is I think we talk a lot about the GPUs involved from NVIDIA if you want to train an AI model and just, you know, how intense that can be.

And yes, it does cost a lot of money.

It is very intense.

But I think it's also important to remember there are millions of people around the world using these AI models.