title: An Empirical Study of Qwen3 Quantization
summary: 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.
paper_authors._id: 6819905519b420a2f91aa231
paper_authors.name: Xingyu Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-07T08:09:43.859Z
paper_authors._id: 64612660933afb0106a9dee3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/Ea83e0zR_2m8foWy6J0AF.jpeg
paper_authors.fullname: Xingyu Zheng
paper_authors.type: user
paper_authors.user: Xingyu-Zheng
paper_authors._id: 6819905519b420a2f91aa232
paper_authors.name: Yuye Li
paper_authors._id: 6819905519b420a2f91aa233
paper_authors.name: Haoran Chu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-07T08:09:41.155Z
paper_authors._id: 68164883244ee586cb159268
paper_authors.avatarUrl: /avatars/856ca8731ca156f616529e427d8fd76a.svg
paper_authors.fullname: 初浩然
paper_authors.type: user
paper_authors.user: HaoranChu
paper_authors._id: 6819905519b420a2f91aa234
paper_authors.name: Yue Feng
paper_authors._id: 6819905519b420a2f91aa235
paper_authors.name: Xudong Ma
paper_authors._id: 6819905519b420a2f91aa236
paper_authors.name: Jie Luo
paper_authors._id: 6819905519b420a2f91aa237
paper_authors.name: Jinyang Guo
paper_authors._id: 6819905519b420a2f91aa238
paper_authors.name: Haotong Qin
paper_authors._id: 6819905519b420a2f91aa239
paper_authors.name: Michele Magno
paper_authors._id: 6819905519b420a2f91aa23a
paper_authors.name: Xianglong Liu
paper_title: An Empirical Study of Qwen3 Quantization
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Large Language Models LLMs natural language understanding Qwen3 low-bit quantization post-training quantization bit-widths linguistic tasks model compression