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Rongchai Wang
Feb 23, 2026 18:39

NVIDIA’s NVFP4 4-bit training format achieves 59% faster AI model training than BF16 while matching accuracy on Llama 3 8B benchmarks, per new research.

NVIDIA’s NVFP4 low-precision training format delivers up to 1.59x faster throughput compared to standard BF16 training while maintaining equivalent model accuracy, according to new benchmarks published by the company’s research team on February 23, 2026. The results mark a significant milestone for 4-bit AI training, demonstrating that aggressive numerical compression doesn’t require sacrificing model quality when proper techniques are applied. The Numbers That Matter Testing on Llama 3 8B models trained across 1 trillion tokens, NVIDIA’s team measured throughput at 1,850 TFLOP/s per GPU with NVFP4 versus 1,165 TFLOP/s for BF16 baseline—a 59% improvement. The tests ran on GB200 NVL72 hardware using the company’s Blackwell architecture. Downstream benchmark scores tell the real story. On MMLU, NVFP4-trained Llama 3 8B scored 45.64% compared to 45.98% for BF16. HellaSwag showed 75.59% versus 76.44%. These differences fall within noise margins for practical applications. Memory efficiency gains enabled doubling the micro-batch size from 2 to 4 during pretraining, directly improving scalability for large-scale training runs. Why 4-Bit Training Works Now Previous attempts at ultra-low-precision training often resulted in model divergence or significant accuracy degradation. NVIDIA’s approach sidesteps these issues through a specific recipe that’s emerged from extensive testing. The critical insight: keeping approximately 15% of the network in higher precision prevents training collapse. Specifically, the final four transformer layers must remain in BF16. Ablation studies confirmed that fully NVFP4 models diverge during training. The format uses a two-level scaling strategy—micro-block scaling for groups of 16 elements combined with global FP32 scaling across full tensors. This hierarchical approach manages the limited dynamic range inherent in 4-bit representations.…

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Filed under: News - @ February 24, 2026 10:28 am