Training Compute
topic on 3 shows · 3 statements across 3 episodes
Another Podcast 
No Priors 
the a16z Podcast
3 statements about Training Compute, every show
Johnson: Scaling spatial world models is primarily bottlenecked by training compute
“I think we're basically at the beginning, and we're basically limited by compute at this point, right? Like, data is very important, as Fei-Fei likes to point out, but, like, everything has a bottleneck, and I think the main bottleneck on continuing to scale t…”
Steinberger: AI performance requires trading off training compute against inference compute
“Well, so you can think of model performance as some function of training compute times some function of inference time compute. Now those are specific functions that are just scaling law things that you can like model, but the general Way to think about it is …”
Benedict Evans: AI model training costs will drop as efficiency improves
“And those are all, those points on that plot, all of like historically have gone up and to the right, but now they're going to start going down because the models get more efficient and people work out better ways of training them when you optimize all the ind…”