Compute Capacity
topic on 4 shows · 6 statements across 6 episodes
Invest Like the Best 
the a16z Podcast 
Big Technology 
All-In
6 statements about Compute Capacity, every show
Friar: Additional compute capacity is virtually impossible to buy in 2026
“But the landscape right now, in 26, if you want to buy more compute, good luck to you. Like, tell me, because I don't know where else to find it.”
Patel: AI capacity and cost matter more than inference latency
“I'd probably still say capacity slash cost is more important than latency. I think existing levels of latency are fast enough for a lot.”
Mensch: Compute-optimal LLM scaling requires equal relative growth in parameters and data
“In common words, if you multiply by four your compute capacity, you should multiply by two, the model size and by two, the data size.”
Appenzeller: Training open-source LLMs requires $2M to $10M in compute
“You need to find a couple of million or ten million dollars of compute capacity to do it, and that makes it so much harder, right?”
Appenzeller: AI founders should shop around across cloud providers
“I think my number one advice would be to shop around. For a certain process which you don't want to use, there's capacity, but for another one that you do want to use, they don't have the capacity.”
Wood: Training Net-New AI Models Will Not Be Common
“Yeah, I think training net new models, ah, is not going to be very common. It's, ah, it's very complicated. It is expensive, to your point. You need a lot of compute capacity, a lot of data, a lot of expertise. Some folks that have differentiation in one of th…”