07Explain mixed-precision training: FP16 vs BF16, loss scaling, and where the numerics break.▼hard★ EssentialNVIDIAOpenAIGoogle2 repliesunlockedMixed precision is routine at scale, and the interviewer wants the numerics: why FP16 needs loss scaling, why BF16 mostly does not, and what remains in FP32. What they grade is whether you understand dynamic range vs precision. This is that answer.Open full answer →
63How does FP8 training work on Hopper GPUs, and how do you keep it numerically stable?▼expertNVIDIAOpenAIMistral1 replies◆ premiumFP8 can nearly double training throughput over BF16, but with only a few mantissa bits the numerics leave no slack. Per-tensor scaling and a selective recipe are what get it to converge.Open full answer →
