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arXiv · 2506.12024

FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization

Fangxin Liu·Zongwu Wang·JinHong Xia·Junping Zhao·Shouren Zhao·Jinjin Li·Jian Liu·Li Jiang·Haibing Guan
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Abstract

The rapid advancement of large language models (LLMs) has exacerbated the memory bottleneck due to the widening gap between model parameter scaling and hardware capabilities. While post-training quantization techniques effectively reduce memory overhead, existing methods predominantly rely on static quantization strategies, which struggle to adapt to dynamic workloads. To address this, we propose FlexQuant, a dynamic precision-switching framework that optimizes the trade-off between inference speed and accuracy. Leveraging model perplexity entropy and Kullback-Leibler divergence, FlexQuant enables fine-grained, layer-wise mixed-precision quantization and dynamically adjusts bit-widths during each token generation. FlexQuant provides a comprehensive analysis of quantization strategies, introduces a precision requirement model for optimal switching, and implements efficient fine-grained precision management. Evaluations demonstrate that FlexQuant achieves a 1.3x end-to-end speedup across diverse language tasks with negligible accuracy loss introduced. This framework offers a flexible and adaptive solution for efficient LLM deployment. Code is released at https://github.com/ZongwuWang/FlexQuant.git.

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Fangxin Liu, Zongwu Wang, JinHong Xia, Junping Zhao, Shouren Zhao, Jinjin Li, Jian Liu, Li Jiang, Haibing Guan. 2025-05-21. FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization. https://arxiv.org/abs/2506.12024

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