Reiner Pope: evaluation

Transcript context

…Exactly, a long time. How do you think about parallelization, and is that narrative true? It's true for sure. It's true in many areas of the market. I think, and especially where you look at where NVIDIA entered the market, they're doing PC devices, lots of gaming, and so on. There are thousands of games, maybe tens of thousands of games released, and they all need to be programmed against CUDA, and so there's such a huge investment in the software that this is really important, the compatibility. There are not thousands of LLMs. There's one LLM per frontier lab, and there's maybe five frontier labs or something like that. Just the economics of that is different. The calculation for a frontier lab roughly goes as, I just bought a $10 billion compute cluster, I have hired 50 of the best people who can write optimized GPU, or TPU, or Trainium software. I pay them less than $10 billion, a lot less. Let's put them to work optimizing the compute. Good work there, depends on what your baseline is, but it can very easily double the performance of the software you write. There is a huge amount of custom software written for every generation of chip. When a new chip comes out, software is substantially rewritten to optimize for that specific chip. That's just the right trade-off given the relative costs of these things. What that means for us is that ecosystem already exists, and that way of operating, where you say, "I'm just going to staff a 50-person team to write software for this chip," works really well if you're trying to sell to frontier labs. You're saying CUDA is way more important for the games environment, where it just does a lot of games than this top-heavy AI market that we're in, where if people say, "You need to then customize your workload for a MatX chip," it's like, "Well, fine, do that."…

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