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Quentin Anthony: evaluation

16 Aug 2023 Latent Space The Mathematics of Training LLMs — with Quentin Anthony of Eleuther AI

“And we did a lot of experiments there. It's a challenging system to scale to because your interconnect across nodes is kind of slow in comparison to within a node, which I think we'll get to later.”

— Quentin Anthony

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Speaker
Quentin Anthony
Attribution
Verified speaker
Claim type
evaluation
Recorded
16 Aug 2023
Publisher
Latent Space

Transcript context

…Maybe just talk about what's available from the government labs and I heard the original, the origin of Eluther started with a grant for TPUs. Is that right? Yes, that was a little before me, but there was a lot of just like getting a grabbing a Google Cloud or TPU pod or something like that is a lot of the original TPU work on Mesh TensorFlow, which is like now like an ancient distributed deep learning library. Eluther got a grant, an insight grant with Oak Ridge last year, and we got quite a bit of Summit Compute. So Summit is a V100 based supercomputer. It's got some weirdness to it. So there's six V100 GPUs per node. And we did a lot of experiments there. It's a challenging system to scale to because your interconnect across nodes is kind of slow in comparison to within a node, which I think we'll get to later. But now Oak Ridge has moved to AMD. So the next grant that we're trying to work towards is on Frontier, which has four AMD GPUs per node and again has a slower interconnect across nodes. So we get all of those new challenges again to try and overlap things. But that's just like you have Oak Ridge, you have Lawrence Livermore. There's a lot of government supercomputers that you can apply for compute towards like open researchers too. It's sort of a new thing. I think we're one of the first like us and like Lion, for example, is another organization that's getting compute from government providers and such. They're all moving to AMD as well. And we look forward to exploring that with them. Yeah.…

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