MergeQuant: Accurate 4-bit Static Quantization of Large Language Models by Channel-wise Calibration

Jinguang Wang; Jingyu Wang; Haifeng Sun; Tingting Yang; Zirui Zhuang; Wanyi Ning; Yuexi Yin; Qi Qi; Jianxin Liao

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arXiv · 2503.07654

MergeQuant: Accurate 4-bit Static Quantization of Large Language Models by Channel-wise Calibration

Jinguang Wang·Jingyu Wang·Haifeng Sun·Tingting Yang·Zirui Zhuang·Wanyi Ning·Yuexi Yin·Qi Qi·Jianxin Liao

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Abstract

Quantization has been widely used to compress and accelerate inference of large language models (LLMs). Existing methods focus on exploring the per-token dynamic calibration to ensure both inference acceleration and model accuracy under 4-bit quantization. However, in autoregressive generation inference of long sequences, the overhead of repeated dynamic quantization and dequantization steps becomes considerably expensive. In this work, we propose MergeQuant, an accurate and efficient per-channel static quantization framework. MergeQuant integrates the per-channel quantization steps with the corresponding scalings and linear mappings through a Quantization Step Migration (QSM) method, thereby eliminating the quantization overheads before and after matrix multiplication. Furthermore, in view of the significant differences between the different channel ranges, we propose dimensional reconstruction and adaptive clipping to address the non-uniformity of quantization scale factors and redistribute the channel variations to the subsequent modules to balance the parameter distribution under QSM. Within the static quantization setting of W4A4, MergeQuant reduces the accuracy gap on zero-shot tasks compared to FP16 baseline to 1.3 points on Llama-2-70B model. On Llama-2-7B model, MergeQuant achieves up to 1.77x speedup in decoding, and up to 2.06x speedup in end-to-end compared to FP16 baseline.

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BibTeX RIS

Jinguang Wang, Jingyu Wang, Haifeng Sun, Tingting Yang, Zirui Zhuang, Wanyi Ning, Yuexi Yin, Qi Qi, Jianxin Liao. 2025-03-07. MergeQuant: Accurate 4-bit Static Quantization of Large Language Models by Channel-wise Calibration. https://arxiv.org/abs/2503.07654

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