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arXiv · 2611.04323

FP4Quant: Efficient 4-Bit Communication Compression for Model-Parallel LLM Training

Man Liu·Xingchen Liu·Hairui Zhao·Guangming Tan·Dingwen Tao
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Abstract

The rapid scaling of large language models (LLMs) has made communication overhead a critical bottleneck in distributed model-parallel training. Although existing low-precision compression methods can effectively reduce communication costs, further reducing the communication footprint to ultra-low-bit formats such as FP4 introduces new challenges, including limited numerical range, sub-byte storage overhead, and increased computational cost from fine-grained quantization. In this paper, we present \textbf{FP4Quant}, a micro-grouped FP4 compression framework for efficient model-parallel LLM training. FP4Quant integrates a micro-grouped dual-scale FP4 quantization mechanism to preserve local tensor distribution characteristics, a warp-cooperative FP4 packing scheme to enable efficient sub-byte data representation, and a hybrid two-level FWHT pipeline to mitigate quantization errors with GPU-aware execution optimization. Experiments on NVIDIA H100 GPUs with Qwen-7B demonstrate up to 1.56$\times$ end-to-end throughput improvement over BF16, revealing a trade-off between throughput and model quality.

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Man Liu, Xingchen Liu, Hairui Zhao, Guangming Tan, Dingwen Tao. 2026-11-03. FP4Quant: Efficient 4-Bit Communication Compression for Model-Parallel LLM Training. https://arxiv.org/abs/2611.04323

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