Built on

Merity, S., Xiong, C., Bradbury, J., Socher, R.: Pointer sentinel mixture models. arXiv preprint arXiv:1609.07843 (2016)
Original
2016
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., Tafjord, O.: Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv:1803.05457 (2018)
Original
2018
Earlier work this paper cites.
Clark, C., Lee, K., Chang, M.W., Kwiatkowski, T., Collins, M., Toutanova, K.: Boolq: Exploring the surprising difficulty of natural yes/no questions. arXiv preprint arXiv:1905.10044 (2019)
Original
2019
Earlier work this paper cites.
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., Choi, Y.: Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830 (2019)
Original
2019
Earlier work this paper cites.
Bisk, Y., Zellers, R., Gao, J., Choi, Y., et al.: Piqa: Reasoning about physical commonsense in natural language. In: Proceedings of the AAAI conference on artificial intelligence. vol. 34, pp. 7432–7439 (2020)
2020
Earlier work this paper cites.
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., Steinhardt, J.: Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300 (2020)
Original
2020
Earlier work this paper cites.

Similar

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21
2020
Cited alongside, same era.
Sakaguchi, K., Bras, R.L., Bhagavatula, C., Choi, Y.: Winogrande: An adversarial winograd schema challenge at scale. Communications of the ACM 64
2021
Cited alongside, same era.
Frantar, E., Ashkboos, S., Hoefler, T., Alistarh, D.: Gptq: Accurate post-training quantization for generative pre-trained transformers. arXiv preprint arXiv:2210.17323 (2022)
Original
2022
Cited alongside, same era.
Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)
Original
2023
Cited alongside, same era.

Then

Yuan, Z., Niu, L., Liu, J., Liu, W., Wang, X., Shang, Y., Sun, G., Wu, Q., Wu, J., Wu, B.: Rptq: Reorder-based post-training quantization for large language models. arXiv preprint arXiv:2304.01089 (2023)
Original
2023
Later among the works it cites.
Gong, R., Ding, Y., Wang, Z., Lv, C., Zheng, X., Du, J., Qin, H., Guo, J., Magno, M., Liu, X.: A survey of low-bit large language models: Basics, systems, and algorithms. arXiv preprint arXiv:2409.16694 (2024)
Original
2024
Later among the works it cites.
Huang, W., Liu, Y., Qin, H., Li, Y., Zhang, S., Liu, X., Magno, M., Qi, X.: Billm: Pushing the limit of post-training quantization for llms. arXiv preprint arXiv:2402.04291 (2024)
Original
2024
Later among the works it cites.
Huang, W., Zheng, X., Ma, X., Qin, H., Lv, C., Chen, H., Luo, J., Qi, X., Liu, X., Magno, M.: An empirical study of llama3 quantization: From llms to mllms. Visual Intelligence 2
2024
Later among the works it cites.

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2025

An Empirical Study of Qwen3 Quantization

Zheng, Xingyu, Li, Yuye, Chu, Haoran et al.

Understand

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.

Built on

Merity, S., Xiong, C., Bradbury, J., Socher, R.: Pointer sentinel mixture models. arXiv preprint arXiv:1609.07843 (2016)
Original
2016
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., Tafjord, O.: Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv:1803.05457 (2018)
Original
2018
Earlier work this paper cites.
Clark, C., Lee, K., Chang, M.W., Kwiatkowski, T., Collins, M., Toutanova, K.: Boolq: Exploring the surprising difficulty of natural yes/no questions. arXiv preprint arXiv:1905.10044 (2019)
Original
2019
Earlier work this paper cites.
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., Choi, Y.: Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830 (2019)
Original
2019
Earlier work this paper cites.
Bisk, Y., Zellers, R., Gao, J., Choi, Y., et al.: Piqa: Reasoning about physical commonsense in natural language. In: Proceedings of the AAAI conference on artificial intelligence. vol. 34, pp. 7432–7439 (2020)
2020
Earlier work this paper cites.
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., Steinhardt, J.: Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300 (2020)
Original
2020
Earlier work this paper cites.

Similar

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21
2020
Cited alongside, same era.
Sakaguchi, K., Bras, R.L., Bhagavatula, C., Choi, Y.: Winogrande: An adversarial winograd schema challenge at scale. Communications of the ACM 64
2021
Cited alongside, same era.
Frantar, E., Ashkboos, S., Hoefler, T., Alistarh, D.: Gptq: Accurate post-training quantization for generative pre-trained transformers. arXiv preprint arXiv:2210.17323 (2022)
Original
2022
Cited alongside, same era.
Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)
Original
2023
Cited alongside, same era.

Then

Yuan, Z., Niu, L., Liu, J., Liu, W., Wang, X., Shang, Y., Sun, G., Wu, Q., Wu, J., Wu, B.: Rptq: Reorder-based post-training quantization for large language models. arXiv preprint arXiv:2304.01089 (2023)
Original
2023
Later among the works it cites.
Gong, R., Ding, Y., Wang, Z., Lv, C., Zheng, X., Du, J., Qin, H., Guo, J., Magno, M., Liu, X.: A survey of low-bit large language models: Basics, systems, and algorithms. arXiv preprint arXiv:2409.16694 (2024)
Original
2024
Later among the works it cites.
Huang, W., Liu, Y., Qin, H., Li, Y., Zhang, S., Liu, X., Magno, M., Qi, X.: Billm: Pushing the limit of post-training quantization for llms. arXiv preprint arXiv:2402.04291 (2024)
Original
2024
Later among the works it cites.
Huang, W., Zheng, X., Ma, X., Qin, H., Lv, C., Chen, H., Luo, J., Qi, X., Liu, X., Magno, M.: An empirical study of llama3 quantization: From llms to mllms. Visual Intelligence 2
2024
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Lin, J., Tang, J., Tang, H., Yang, S., Dang, X., Han, S.: Awq: Activation-aware weight quantization for llm compression and acceleration. arXiv preprint arXiv:2306.00978 (2023)

Original

2023

Cited alongside, same era.

Xiao, G., Lin, J., Seznec, M., Wu, H., Demouth, J., Han, S.: Smoothquant: Accurate and efficient post-training quantization for large language models. In: International Conference on Machine Learning. pp. 38087–38099. PMLR (2023)

2023

Cited alongside, same era.

Liu, Z., Zhao, C., Fedorov, I., Soran, B., Choudhary, D., Krishnamoorthi, R., Chandra, V., Tian, Y., Blankevoort, T.: Spinquant: Llm quantization with learned rotations. arXiv preprint arXiv:2405.16406 (2024)

Original

2024

Later among the works it cites.

Yang, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Li, C., Liu, D., Huang, F., Wei, H., et al.: Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115 (2024)

Original

2024

Later among the works it cites.