An Empirical Study of Qwen3 Quantization
The Qwen series has emerged as a leading family of open-source Large Language
Models (LLMs), demonstrating remarkable capabilities in natural language
understanding tasks. With the recent release of Qwen3, which exhibits superior
performance across diverse benchmarks, there is growing interest in deploying
these models efficiently in resource-constrained environments. Low-bit
quantization presents a promising solution, yet its impact on Qwen3's
performance remains underexplored. This study conducts a systematic evaluation
of Qwen3's robustness under various quantization settings, aiming to uncover
both opportunities and challenges in compressing this state-of-the-art model.
We rigorously assess 5 existing classic post-training quantization techniques
applied to Qwen3, spanning bit-widths from 1 to 8 bits, and evaluate their
effectiveness across multiple datasets. Our findings reveal that while Qwen3
maintains competitive performance at moderate bit-widths, it experiences
notable degradation in linguistic tasks under ultra-low precision, underscoring
the persistent hurdles in LLM compression. These results emphasize the need for
further research to mitigate performance loss in extreme quantization
scenarios. We anticipate that this empirical analysis will provide actionable
insights for advancing quantization methods tailored to Qwen3 and future LLMs,
ultimately enhancing their practicality without compromising accuracy. Our
project is released on https://github.com/Efficient-ML/Qwen3-Quantization and
https://huggingface.co/collections/Efficient-ML/qwen3-quantization-68164450decb1c868788cb2b.
6819905519b420a2f91aa231
Xingyu Zheng
claimed_verified
2025-05-07T08:09:43.859Z
64612660933afb0106a9dee3
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/Ea83e0zR_2m8foWy6J0AF.jpeg
Xingyu Zheng
user
Xingyu-Zheng
6819905519b420a2f91aa232
Yuye Li
6819905519b420a2f91aa233
Haoran Chu
claimed_verified
2025-05-07T08:09:41.155Z
68164883244ee586cb159268
/avatars/856ca8731ca156f616529e427d8fd76a.svg
初浩然
user
HaoranChu
6819905519b420a2f91aa234
Yue Feng
6819905519b420a2f91aa235
Xudong Ma
6819905519b420a2f91aa236
Jie Luo
6819905519b420a2f91aa237
Jinyang Guo
6819905519b420a2f91aa238
Haotong Qin
6819905519b420a2f91aa239
Michele Magno
6819905519b420a2f91aa23a
Xianglong Liu
An Empirical Study of Qwen3 Quantization
The Qwen series has emerged as a leading family of open-source Large Language
Models (LLMs), demonstrating remarkable capabilities in natural language
understanding tasks. With the recent release of Qwen3, which exhibits superior
performance across diverse benchmarks, there is growing interest in deploying
these models efficiently in resource-constrained environments. Low-bit
quantization presents a promising solution, yet its impact on Qwen3's
performance remains underexplored. This study conducts a systematic evaluation
of Qwen3's robustness under various quantization settings, aiming to uncover
both opportunities and challenges in compressing this state-of-the-art model.
We rigorously assess 5 existing classic post-training quantization techniques
applied to Qwen3, spanning bit-widths from 1 to 8 bits, and evaluate their
effectiveness across multiple datasets. Our findings reveal that while Qwen3
maintains competitive performance at moderate bit-widths, it experiences
notable degradation in linguistic tasks under ultra-low precision, underscoring
the persistent hurdles in LLM compression. These results emphasize the need for
further research to mitigate performance loss in extreme quantization
scenarios. We anticipate that this empirical analysis will provide actionable
insights for advancing quantization methods tailored to Qwen3 and future LLMs,
ultimately enhancing their practicality without compromising accuracy. Our
project is released on https://github.com/Efficient-ML/Qwen3-Quantization and
https://huggingface.co/collections/Efficient-ML/qwen3-quantization-68164450decb1c868788cb2b.
This study evaluates the impact of low-bit quantization on Qwen3, a state-of-the-art LLM, across various bit-widths and datasets, revealing performance trade-offs and suggesting areas for further research to improve quantization methods.
Large Language Models LLMs natural language understanding Qwen3 low-bit quantization post-training quantization bit-widths linguistic tasks model compression