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Using NVFP4 Low-Precision Model Training for Higher Throughput Without Losing Accuracy

NVIDIA Developer Blog · published 23 February 2026

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https://docs.nvidia.com/cuda/cuda-math-api/cuda_math_api/group__CUDA__MATH__INTRINSIC__BFLOAT16.html

docs.nvidia.com

https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/examples/fp8_primer.html

docs.nvidia.com

NVFP4 Trains with Precision of 16-Bit and Speed and Efficiency of 4-Bit Read at the source ↗

developer.nvidia.com

https://github.com/NVIDIA-NeMo/Megatron-Bridge

github.com

https://github.com/NVIDIA-NeMo

github.com

Floating-Point 8: An Introduction to Efficient, Lower-Precision AI Training Read at the source ↗

developer.nvidia.com

https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/

nvidia.com

https://arxiv.org/abs/2509.25149

arxiv.org

https://huggingface.co/meta-llama/Meta-Llama-3-8B

huggingface.co

https://github.com/facebookresearch/lingua

github.com

https://www.nvidia.com/en-us/data-center/gb200-nvl72/

nvidia.com

https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/5c7ebe7d8c31e75ddf882e1e6ced69d10e250786/src/megatron/bridge/recipes/llama/llama3.py

github.com

https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/tutorials/training/reduced_precision_training.ipynb

github.com