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> It's in the article The same article also states that AMD provided custom bug-fixes written by Principle Engineers to address bugs in a benchmark - this is s

> It's in the article The same article also states that AMD provided custom bug-fixes written by Principle Engineers to address bugs in a benchmark - this is software that will only become part of the public release in 2 quarters. I ask again, do you think AMD will not expedite non-public bug-fixes for hyperscalers? > You can't train with AMD, full stop, because their software stack is so buggy. Point 7 from the article: >> The MI300X has a lower total cost of ownership (TCO) compared to the H100/H200, but training performance per TCO is worse on the MI300X on public stable releases of AMD software. This changes if one uses custom development builds of AMD software. I have an inkling that Meta does not obtain MI300 drivers from https://download.amd.com https://download.amd.com

mike_hearn 2y ago
AI labs don't want to train models using a stack build some guy hacked up on his desktop last night that's been through no proper QA process. The cost of a training run that fails or results in a garbage model due to numerical errors are huge. Which is why, as they say clearly, nobody is training models on AMD. Only inference, at most. I'm not sure why you keep claiming they are training using private drivers. They clearly aren't.
- sangnoir 2y ago
  Now I i see how where talking past each other. I 100% agree that none of if the hyperscalers are currently (publicly) training on AMD silicon. I disagree with forward-looking statements like it "can't" happen, because I can guarantee you several of them are actively working on making it possible to train on AMD chips - that's just too juicy a target for a bonus packets all the way up to directors: "Our team saved the org $x0 million in TCO vy enabling training on MI300/MI400X in our new clusters"
  - mike_hearn 2y ago
    Oh, I see. "Can't" means in the present tense in my previous sentence, it wasn't meant to be a definitive statement about the entire future.

mike_hearn 2y ago

AI labs don't want to train models using a stack build some guy hacked up on his desktop last night that's been through no proper QA process. The cost of a training run that fails or results in a garbage model due to numerical errors are huge. Which is why, as they say clearly, nobody is training models on AMD. Only inference, at most. I'm not sure why you keep claiming they are training using private drivers. They clearly aren't.

sangnoir 2y ago
Now I i see how where talking past each other. I 100% agree that none of if the hyperscalers are currently (publicly) training on AMD silicon. I disagree with forward-looking statements like it "can't" happen, because I can guarantee you several of them are actively working on making it possible to train on AMD chips - that's just too juicy a target for a bonus packets all the way up to directors: "Our team saved the org $x0 million in TCO vy enabling training on MI300/MI400X in our new clusters"
- mike_hearn 2y ago
  Oh, I see. "Can't" means in the present tense in my previous sentence, it wasn't meant to be a definitive statement about the entire future.