GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference

• URL: http://arxiv.org/abs/2412.17560v4
• Date: Sun, 27 Jul 2025 12:52:56 GMT
• Title: GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference
• Authors: Chao Zeng, Songwei Liu, Shu Yang, Fangmin Chen, Lean Fu, Xing Mei,
• Abstract summary: Group Quantization and Sparse Acceleration (GQSA) is a novel compression technique tailored for LLMs.<n>We propose a two-stage sparse optimization strategy that ensures the performance superiority of the compressed model.<n> Experimental results demonstrate that, under the GQSA W4S50% compression setting, the model's accuracy surpasses that of both 2:4 pruning and W2 quantization.
• Score: 5.358880304650258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model compression has emerged as a mainstream solution to reduce memory usage and computational overhead. This paper presents Group Quantization and Sparse Acceleration (GQSA), a novel compression technique tailored for LLMs. Traditional methods typically focus exclusively on either quantization or sparsification, but relying on a single strategy often results in significant performance loss at high compression rates. In contrast, GQSA integrates quantization and sparsification in a tightly coupled manner, leveraging GPU-friendly structured group sparsity and quantization for efficient acceleration. Building upon system-algorithm co-design principles, we propose a two-stage sparse optimization strategy that ensures the performance superiority of the compressed model. On the engine side, we introduce a "task-centric" parallel strategy, which, to the best of our knowledge, is the first application in the domain of sparse computing. Compared to the traditional 2:4 sparse method, the GQSA offers a more flexible and adjustable sparsity rate, as well as a higher weight compression rate, and is efficiently compatible with weight-only quantization methods. Experimental results demonstrate that, under the GQSA W4S50% compression setting, the model's accuracy surpasses that of both 2:4 pruning and W2 quantization. Furthermore, at the inference level, GQSA outperforms W2 by 1.26$\times$ and 2:4 pruning by 2.35$\times$ in terms of speed.

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