Pre-training frontier LLMs comes down to throughput. When training spans trillions of tokens across thousands of accelerators, every percentage point of step time can add up to days of training and substantial compute costs. Numerical precision is one of the highest-leverage knobs available, but low- bit mixed-precision pretraining is hard to get right.

To address this, the NVFP4 training recipe in TransformerEngine uses subbyte precision for JAX pretraining. For an end-to-end example, see the recipe in MaxText, a high-performance, scalable LLM framework library. The result is high-throughput, 4-bit mixed-precision pre-training on NVIDIA Blackwell with no measurable accuracy loss compared to the FP8 baseline.

This post explains the NVFP4 format and how it’s built to achieve high performance and accuracy at ultra-low precision. It also shows how to apply a MaxText NVFP4 pretraining recipe and collect performance data showing performance gains.…