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For narrow-precision formats to be practical in large-scale pretraining, they must ensure both model accuracy and stable convergence. To assess the viability of 4-bit precision in large-scale model training, experiments were conducted with FP8 and NVFP4 on a 12-billion parameter model based on a combined Mamba-Transformer architecture (12B Hybrid Mamba-Transformer model)—similar to NVIDIA Nemotron Nano 2. This model was trained on a massive dataset of 10 trillion tokens using a phased data-blending approach, switching to a different dataset mix in the second phase of training at 70%, and in the third phase of training at 90% during pretraining.

A version of the 12B Hybrid Mamba-Transformer model was initially trained with 8-bit precision—FP8, which has been shown in previous studies to closely match 16-bit precision, and hence served as our baseline for comparison. We then successfully trained this same 12B model from scratch using NVFP4, demonstrating that this new low-precision format can support full pretraining at trillion-token scale. The NVFP4 run exhibited stable convergence without the training instabilities or divergence issues that typically plague ultra-low precision training.

Figure 3 below shows that NVFP4’s validation loss curve closely matches the loss curves from the higher-precision baseline (i.e., FP8) throughout the entire duration of training. The quantization techniques outlined above ensure that even with aggressive bit-width reduction, the 4-bit pretraining dynamics closely resemble those of higher-precision runs.

A version of the 12B Hybrid Mamba-Transformer model was initially trained with 8-bit precision—FP8, which has been shown in previous studies to closely match 16-bit precision, and hence served as our baseline for comparison. We then successfully trained this same 12B model from scratch using NVFP4, demonstrating that this new low-precision format can support full pretraining at trillion-token scale. The NVFP4 run exhibited stable convergence without the training instabilities or divergence issues that typically plague ultra-low precision training.

Introducing NVFP4 for Efficient and Accurate Low-Precision Inference

From the article:

"NVIDIA Blackwell introduces revolutionary block-scaled floating point formats, including the Open Computing Project’s microscaling formats, which Triton now unlocks for NVIDIA Blackwell-powered hardware acceleration.

These formats provide higher average precision at higher performance than the non-native block-scaling techniques emulated frequently in LLM inference projects today.

For OCP format support, MXFP8 GEMMs on Triton showcase exceptional performance similar to the FP8 GEMMs performance accelerated and shown earlier in this post, while natively allowing for scaling in the Tensor Core.

Similarly, MXFP4 provides a new operating point in the precision-performance trade-off space but while offering double the hardware-accelerated performance of FP8 and MXFP8 GEMMs.

While Triton performance for MXFP8 is close to the NVIDIA Blackwell-accelerated FP8 shown earlier, we continue working with the community to accelerate and enable new use cases around block-scaling support."