Exploring Post-Training Quantization of Large Language Models with a Focus on Russian Evaluation
https://doi.org/10.26907/1562-5419-2025-28-5-1138-1163
Abstract
The rapid adoption of large language models (LLMs) has made quantization a central technique for enabling efficient deployment under real-world hardware and memory constraints. While English-centric evaluations of low-bit quantization are increasingly available, much less is known about its effects on morphologically rich and resource-diverse languages such as Russian. This gap is particularly important given the recent emergence of high-performing Russian and multilingual LLMs. In this work, we conduct a systematic study of 2-, 3-, and 4-bit post-training quantization (PTQ) for state-of-the-art Russian LLMs across different model scales (4B and 32B). Our experimental setup covers both standard uniform quantization and specialized low-bit formats, as well as lightweight finetuning for recovery in the most extreme 2-bit setting. Our findings highlight several important trends: (i) the tolerance of Russian LLMs to quantization differs across model families and scales; (ii) 4-bit quantization is generally robust, especially when advanced formats are used; (iii) 3-bit models expose sensitivity to calibration data and scaling strategies; and (iv) 2-bit models, while severely degraded under naive PTQ, can be partially restored through short finetuning. Empirical results show that the model's domain must be considered when using different quantization techniques.
About the Authors
Dmitrii Romanovich Poimanov
Lomonosov Moscow State University
Russian Federation
Russian Federation
Mikhail Sergeevich Shutov
Moscow Institute of Science and Technology
Russian Federation
Russian Federation
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Review
For citations:
Poimanov D.R., Shutov M.S. Exploring Post-Training Quantization of Large Language Models with a Focus on Russian Evaluation. Russian Digital Libraries Journal. 2025;28(5):1138-1163. (In Russ.) https://doi.org/10.26907/1562-5419-2025-28-5-1138-1163
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