How to Optimize Transformer-Based Models for Low-Precision Training | NVIDIA Technical Blog

What this post added

This post details how to optimize transformer-based models for low-precision training using FP8 and NVFP4 on NVIDIA Hopper and Blackwell GPUs. It introduces a microbenchmarking tool that converts model configurations and batch sizes into specific GEMM shapes, enabling empirical benchmarking across different precisions (BF16, MXFP8, NVFP4) and stages (Fprop, Dgrad, Wgrad). The post highlights the importance of analyzing both autocast and pre-quantized results to understand quantization overhead and raw kernel performance, demonstrating that while larger GEMMs benefit significantly, smaller GEMMs may see less improvement due to overheads. It provides a practical example using ESM2-15B to illustrate how to interpret these results for real-world speedup estimation.