Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

TL;DR

This paper introduces a practical FP4 training method for large-scale MoE models on Hopper GPUs, reducing memory and bandwidth usage while maintaining performance without native FP4 hardware support.

Contribution

The authors develop a novel FP8-to-FP4 quantization approach enabling efficient MoE training on Hopper GPUs without native FP4 support.

Findings

Achieves 14.8% reduction in peak activation memory

Improves training throughput by 12.5%

Maintains comparable performance to FP8 baselines at 671B parameters

Abstract

Training large-scale Mixture-of-Experts (MoE) models is bottlenecked by activation memory and expert-parallel communication, yet FP4 training remains impractical on Hopper-class GPUs without native MXFP4 or NVFP4 support. In this work, we present a training recipe that enables MXFP4 efficiency for MoE models on Hopper architectures without native 4-bit computation support. A central challenge is to integrate FP4 into an existing BF16/FP8 hybrid training pipeline without incurring costly precision round-trips (e.g., FP4 $\leftrightarrow$ BF16 $\leftrightarrow$ FP8). We address this challenge by introducing direct FP8-to-FP4 quantization and de-quantization, together with scaling-aware FP4 row-wise to column-wise conversion, enabling FP4 activations and expert-parallel communication with minimal overhead. Core MoE computations are executed in FP8, while activations and expert-parallel communication are compressed using MXFP4, achieving substantial memory and bandwidth savings without degrading convergence. At the 671B parameter scale, our method achieves end-to-end training performance comparable to strong FP8 baselines, while reducing peak activation memory by 14.8\% (11.8 GB) and improving training throughput by 12.5\%, from 1157 to 1302 tokens per GPU per second. These results show that FP4 efficiency can be practically realized for large-scale MoE training through careful software-hardware co-design, even without native FP4 Tensor Core support.