Thèmes / numerical precision optimizationPoint de vue reformulé
MXFP8 improves throughput and peak active memory in a controlled B300 benchmark
The post reports that in a controlled benchmark on four NVIDIA B300 GPUs with uniform expert load, enabling MXFP8 in performance-critical parts increased end-to-end training throughput by ~21% over BF16 baseline and reduced peak active memory from 103 GiB to 95 GiB; most gains came from feed-forward computation and inter-expert data movement, not attention alone.
Derrière ce point de vue
Les traductions sont destinées à la lecture ; les extraits originaux restent la source evidence.
Introducing Olmo-core 3: Open, scalable training infrastructure for large MoEs
Extrait original
With MXFP8 enabled across the parts of the system where it helped most, training throughput was about 21% higher than with BF16, the higher-precision format we used as our baseline, while peak active memory fell from 103 GiB to 95 GiB. Most of the gain came from feed-forward computation and moving data between experts rather than attention alone.
Contexte
We measured MXFP8’s effect on end-to-end training throughput in a controlled benchmark on four NVIDIA B300 GPUs, with work distributed uniformly across experts.