Quentin Anthony: recommendation

Transcript context

…One thing that I noticed, you mentioned some of these laws are only true when a thousand GPUs for one hour cost the same as one GPU for a thousand hours, given that we have a shortage of the biggest GPUs out there. Any thoughts there on how people should prioritize this? Yeah, so I would say you should find what the minimum number of GPUs is to just fit your model first. The memory bottleneck is your biggest problem if you have a sizable model. If it's a small model, nobody cares. But most models that people care about will need to be split across multiple GPUs. So find the minimum number of GPUs to just fit your one instance of your model and then calculate how long that's going to take. If it's a reasonable amount of time, then you're done. If it takes too long, then you need to start worrying about having multiple instances of that model. I always feel like you should go with the minimum number of GPUs because the more number of GPUs that you have, the more likely it is for things to break. So I would say just find out what time is reasonable for you and then fit the number of GPUs to that and no more. Because people get greedy and they say, if I have twice the GPUs, I can get this done in half the time. And then you end up taking three times the time because everything is breaking every day. And that's when I am up at midnight trying to fix your model that's broken. We had a previous guest which has invested a lot in their framework for training these things. Would there not be an equivalent open source framework you guys would have made that would help with scaling up GPUs linearly like that? Or is this an oversimplification?…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.