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arXiv · 2603.16435

VQKV: High-Fidelity and High-Ratio Cache Compression via Vector-Quantization

Yixuan Wang·Qingyu Shi·Jiayu Zhou·Dianbo Liu·Ziwei He·Zhouhan Lin
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Abstract

The growing context length of Large Language Models (LLMs) enlarges the Key-Value (KV) cache, limiting deployment in resource-limited environments. Prior training-free approaches for KV cache compression typically rely on low-rank approximation or scalar quantization, which fail to simultaneously achieve high compression ratios and high reconstruction fidelity. We propose VQKV, a novel, training-free method introducing vector quantization (VQ) to obtain highly compressed KV representations while preserving high model fidelity, allowing for the representation of thousands of floating-point values with just a few integer indices. As a result, VQKV achieves an 82.8\% compression ratio on LLaMA3.1-8B while retaining 98.6\% of the baseline performance on LongBench and enabling 4.3x longer generation length on the same memory footprint.

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Yixuan Wang, Qingyu Shi, Jiayu Zhou, Dianbo Liu, Ziwei He, Zhouhan Lin. 2026-03-17. VQKV: High-Fidelity and High-Ratio Cache Compression via Vector-Quantization. https://arxiv.org/abs/2603.16435

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