Recipes for Pre-training LLMs with MXFP8

Asit Mishra; Dusan Stosic; Simon Layton; Paulius Micikevicius

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arXiv · 2506.08027

Recipes for Pre-training LLMs with MXFP8

Asit Mishra·Dusan Stosic·Simon Layton·Paulius Micikevicius

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Abstract

Using fewer bits to represent model parameters and related tensors during pre-training has become a required technique for improving GPU efficiency without sacrificing accuracy. Microscaling (MX) formats introduced in NVIDIA Blackwell generation of GPUs represent a major advancement of this technique, making it practical to combine narrow floating-point data types with finer granularity per-block scaling factors. In turn, this enables both quantization of more tensors than previous approaches and more efficient execution of operations on those tensors. Effective use of MX-formats requires careful choices of various parameters. In this paper we review these choices and show how MXFP8-E4M3 datatype and a specific number conversion algorithm result in training sessions that match those carried out in BF16. We present results using models with up to 8B parameters, trained on high-quality datasets of up to 15T tokens.

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Asit Mishra, Dusan Stosic, Simon Layton, Paulius Micikevicius. 2025-08-18. Recipes for Pre-training LLMs with MXFP8. https://arxiv.org/abs/2506.08027

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