[WIP] Document MX FP8 recipe #1350
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| MXFP8 training can provide substantial training speedups for models running on Nvidia Blackwell architecture (G and B200s+). MX FP8 enables fine grained quantization, where 1 x 32 elements are quantized per a single U8ME0 scaling, and this scaling can be done via hardware. | ||
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| We have tested MXFP8 training at 1856 GPU Scale (Crusoe B200 cluster) and for Llama 3 70B model, we observed ~ 19% speedup with near equal or better convergence loss relative to BF16. |
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I think it's better if such claim (and most of this doc) is put in https://github.com/pytorch/torchtitan/tree/main/benchmarks
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In progress - let's show how to use mxfp8 with Titan.