@inproceedings{zeng-etal-2025-gqsa,
title = "{GQSA}: Group Quantization and Sparsity for Accelerating Large Language Model Inference",
author = "Zeng, Chao and
Liu, Songwei and
Yang, Shu and
Chen, Fangmin and
Mei, Xing and
Fu, Lean",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.10/",
pages = "149--165",
ISBN = "979-8-89176-298-5",
abstract = "Model compression has emerged as a mainstream solution to reduce memory usage and computational overhead. This paper proposes GQSA, a novel model compression framework specifically designed 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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<abstract>Model compression has emerged as a mainstream solution to reduce memory usage and computational overhead. This paper proposes GQSA, a novel model compression framework specifically designed 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.</abstract>
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%0 Conference Proceedings
%T GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference
%A Zeng, Chao
%A Liu, Songwei
%A Yang, Shu
%A Chen, Fangmin
%A Mei, Xing
%A Fu, Lean
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F zeng-etal-2025-gqsa
%X Model compression has emerged as a mainstream solution to reduce memory usage and computational overhead. This paper proposes GQSA, a novel model compression framework specifically designed 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.
%U https://aclanthology.org/2025.ijcnlp-long.10/
%P 149-165
Markdown (Informal)
[GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference](https://aclanthology.org/2025.ijcnlp-long.10/) (Zeng et al., IJCNLP-AACL 2025)
ACL
- Chao Zeng, Songwei Liu, Shu Yang, Fangmin Chen, Xing Mei, and Lean Fu. 2025. GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 149–165, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.