GPU Capacity
topic on 5 shows · 6 statements across 5 episodes
Lenny's Podcast 
the Neon Show 
No Priors 
the a16z Podcast 
20VC
6 statements about GPU Capacity, every show
Kamath: AI vendors overprice models to offset GPU auto-scaling costs
“And the biggest issue is today people end up pricing the models extremely expensively because they have to manage the scale up of GP capacity.”
Tunguz: Most hyperscaler GPU capacity is sold out for the next two years
“I mean, most of the hyperscalers GPU capacity is sold out for the next two years.”
Adding AI researchers decreases group productivity when GPU capacity is constrained
“In a world where you're constrained by the amount of GPU capacity that you have as a researcher, which is the case for OpenAI researchers, but also researchers everywhere else, like Each new researcher that you add is actually, like, a net productivity loss fo…”
Appenzeller: High-scale AI operations must pre-reserve dedicated GPU capacity
“If you need a lot, you frankly, you have to pre-reserve them, right? You have to have your own, there's just no, no way around that.”
Appenzeller: AI batch processing jobs can rely on ad-hoc GPU capacity
“On the other hand, if I have more batch jobs where it's like, ah, this job runs an hour later, that's not the end of the world. Then you probably can go with variable capacity and you know, just preserve it ad hoc.”