@inproceedings{lee-etal-2025-amq,
title = "{AMQ}: Enabling {A}uto{ML} for Mixed-precision Weight-Only Quantization of Large Language Models",
author = "Lee, Sangjun and
Woo, Seung-taek and
Jin, Jun-gyu and
Lee, Changhun and
Park, Eunhyeok",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1799/",
pages = "35520--35538",
ISBN = "979-8-89176-332-6",
abstract = "To enable broader deployment of Large Language Models (LLMs), it is essential to identify the best-performing model under strict memory constraints. We present AMQ, Automated Mixed-Precision Weight-Only Quantization, a framework that assigns layer-wise quantization bit-widths to optimally balance model quality and memory usage. However, the combinatorial search space, with over $10^{100}$ possible configurations, makes conventional black-box optimization infeasible. AMQ overcomes this challenge through four key innovations: (1) **search space pruning** using prior knowledge to exclude unpromising configurations, (2) **quantization proxy** to bypass costly format conversions during search, (3) **quality predictor** to minimize evaluation overhead, and (4) **iterative search-and-update** strategy for fast and stable convergence. By integrating these components, AMQ efficiently explores the quality{--}efficiency landscape, reaching the Pareto frontier and yielding LLMs that are both compact and high-performing."
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<abstract>To enable broader deployment of Large Language Models (LLMs), it is essential to identify the best-performing model under strict memory constraints. We present AMQ, Automated Mixed-Precision Weight-Only Quantization, a framework that assigns layer-wise quantization bit-widths to optimally balance model quality and memory usage. However, the combinatorial search space, with over 10¹00 possible configurations, makes conventional black-box optimization infeasible. AMQ overcomes this challenge through four key innovations: (1) **search space pruning** using prior knowledge to exclude unpromising configurations, (2) **quantization proxy** to bypass costly format conversions during search, (3) **quality predictor** to minimize evaluation overhead, and (4) **iterative search-and-update** strategy for fast and stable convergence. By integrating these components, AMQ efficiently explores the quality–efficiency landscape, reaching the Pareto frontier and yielding LLMs that are both compact and high-performing.</abstract>
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%0 Conference Proceedings
%T AMQ: Enabling AutoML for Mixed-precision Weight-Only Quantization of Large Language Models
%A Lee, Sangjun
%A Woo, Seung-taek
%A Jin, Jun-gyu
%A Lee, Changhun
%A Park, Eunhyeok
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F lee-etal-2025-amq
%X To enable broader deployment of Large Language Models (LLMs), it is essential to identify the best-performing model under strict memory constraints. We present AMQ, Automated Mixed-Precision Weight-Only Quantization, a framework that assigns layer-wise quantization bit-widths to optimally balance model quality and memory usage. However, the combinatorial search space, with over 10¹00 possible configurations, makes conventional black-box optimization infeasible. AMQ overcomes this challenge through four key innovations: (1) **search space pruning** using prior knowledge to exclude unpromising configurations, (2) **quantization proxy** to bypass costly format conversions during search, (3) **quality predictor** to minimize evaluation overhead, and (4) **iterative search-and-update** strategy for fast and stable convergence. By integrating these components, AMQ efficiently explores the quality–efficiency landscape, reaching the Pareto frontier and yielding LLMs that are both compact and high-performing.
%U https://aclanthology.org/2025.emnlp-main.1799/
%P 35520-35538
Markdown (Informal)
[AMQ: Enabling AutoML for Mixed-precision Weight-Only Quantization of Large Language Models](https://aclanthology.org/2025.emnlp-main.1799/) (Lee et al., EMNLP 2025)
ACL