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arXiv · 2506.12038

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation

Fangxin Liu·Ning Yang·Junping Zhao·Tao Yang·Haibing Guan·Li Jiang
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Abstract

Large language models (LLMs) have achieved significant progress in natural language processing but face challenges in deployment due to high memory and computational requirements. Weight quantization is a common approach to address these issues, yet achieving effective low-bit compression remains challenging. This paper presents LCD, which unifies the learning of clustering-based quantization within a knowledge distillation framework. Using carefully designed optimization techniques, LCD preserves LLM performance even at ultra-low bit widths of 2-3 bits. Additionally, LCD compresses activations through smoothing and accelerates inference with a LUT-based design. Experimental results show that LCD outperforms existing methods and delivers up to a 6.2x speedup in inference. Notably, LCD is shown to be more cost-effective, making it a practical solution for real-world applications.

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Fangxin Liu, Ning Yang, Junping Zhao, Tao Yang, Haibing Guan, Li Jiang. 2025-05-23. LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation. https://arxiv.org/abs/2506.12038

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