An Empirical Study of Qwen3 Quantization

The Qwen series has emerged as a leading family of open-source Large LanguageModels (LLMs), demonstrating remarkable capabilities in natural languageunderstanding tasks. With the recent release of Qwen3, which exhibits superiorperformance across diverse benchmarks, there is growing interest in deployingthese models efficiently in resource-constrained environments. Low-bitquantization presents a promising solution, yet its impact on Qwen3'sperformance remains underexplored. This study conducts a systematic evaluationof Qwen3's robustness under various quantization settings, aiming to uncoverboth opportunities and challenges in compressing this state-of-the-art model.We rigorously assess 5 existing classic post-training quantization techniquesapplied to Qwen3, spanning bit-widths from 1 to 8 bits, and evaluate theireffectiveness across multiple datasets. Our findings reveal that while Qwen3maintains competitive performance at moderate bit-widths, it experiencesnotable degradation in linguistic tasks under ultra-low precision, underscoringthe persistent hurdles in LLM compression. These results emphasize the need forfurther research to mitigate performance loss in extreme quantizationscenarios. We anticipate that this empirical analysis will provide actionableinsights for advancing quantization methods tailored to Qwen3 and future LLMs,ultimately enhancing their practicality without compromising accuracy. Ourproject is released on https://github.com/Efficient-ML/Qwen3-Quantization andhttps://huggingface.co/collections/Efficient-ML/qwen3-quantization-68164450decb1c868788cb2b.