NVIDIA Proved 4-Bit Training Works at Real Scale (Not Just Inference)

2026-06-19 · Source: AIGuys - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

NVIDIA has demonstrated the viability of 4-bit floating-point training for large-scale models, challenging the long-held assumption that high precision is essential for model training. They successfully pretrained a 12-billion-parameter hybrid Mamba-Transformer model on 10 trillion tokens using their NVFP4 format. This model achieved a 62.58% score on MMLU-Pro, nearly matching an identical FP8 model's 62.62% score, a difference of only 0.04 points after extensive training. This breakthrough, attributed to four specific fixes, marks the longest publicly documented 4-bit precision training run to date, moving quantization beyond just inference-time compression.

Key takeaway

For machine learning engineers developing large language models, this breakthrough means you can now consider 4-bit precision for end-to-end training, not just inference. This significantly reduces the computational resources and memory footprint required for pretraining frontier-scale models, potentially accelerating development cycles and lowering infrastructure costs. You should investigate NVIDIA's NVFP4 format and the documented fixes to optimize your training workflows.

Key insights

4-bit floating-point training is now proven effective for frontier-scale models, matching FP8 performance.

Principles

• Training at 4-bit precision is achievable with specific fixes.
• Quantization error can be managed over trillions of tokens.

Method

NVIDIA's NVFP4 format enables end-to-end 4-bit training, overcoming dynamic range and error accumulation challenges.

In practice

• Explore 4-bit precision for large model pretraining.
• Reduce computational costs for model development.

Topics

• 4-bit Training
• Model Quantization
• NVFP4
• Mamba-Transformer
• Large Language Models
• Deep Learning Training

Best for: Research Scientist, AI Engineer, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Hardware Engineer

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• ReSET: Accurate Latency-Critical NVFP4 Reasoning via Step-Aware Temperature Scaling — ReSET enhances NVFP4 LRM accuracy and latency via step-aware temperature scaling and a specialized CUDA kernel.
• Train Models Faster with JAX and MaxText Using NVFP4 on NVIDIA Blackwell — NVFP4 enables significantly faster, accurate 4-bit LLM pre-training on NVIDIA Blackwell GPUs.
• From 32 Bits to 1.58: The Illustrated Guide to LLM Quantization — LLM quantization significantly reduces memory and accelerates inference while maintaining intelligence across various bit precisions.
• Qwen3.6 27B Quantization: FP8 vs INT4 vs NVFP4 — Selective quantization of linear-attention layers is critical for maintaining Qwen3.6 27B model accuracy.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AIGuys - Medium.