NVIDIA Introduces a 4-Bit Pretraining Methodology Using NVFP4, Validated on a 12B Hybrid Mamba-Transformer at 10T Token Horizon

NVIDIA introduces a 4-bit pretraining methodology built around the NVFP4 microscaling format — combining selective BF16 layers, 16×16 Random Hadamard Transforms on Wgrad inputs, 2D weight scaling, and stochastic rounding on gradients — validated on a 12B hybrid Mamba-Transformer trained on 10 trillion tokens, the longest publicly documented 4-bit pretraining run, with downstream accuracy closely tracking the FP8 baseline (62.

NVIDIA introduces a 4-bit pretraining methodology built around the NVFP4 microscaling format — combining selective BF16 layers, 16×16 Random Hadamard Transforms on Wgrad inputs, 2D weight scaling, and stochastic rounding on gradients — validated on a 12B hybrid Mamba-Transformer trained on 10 trillion tokens, the longest publicly documented 4-bit pretraining run, with downstream accuracy closely tracking the FP8 baseline (62.