I Tested 12 Quantization Methods: The Winner Surprised Me (2-Bit vs 4-Bit)
Last Updated on December 29, 2025 by Editorial Team
Author(s): Manash Pratim
Originally published on Towards AI.
Small LLM Engineering #7
Everyone says 4-bit quantization is the practical limit.
This article explores the implications of different quantization methods for machine learning models, particularly focusing on 2-bit and 4-bit quantization. The author recounts testing various quantization formats, emphasizing the practical limits of quantization and the risks associated with smaller bit-widths. Through detailed experimentation, it evaluates how much memory different models consume and the impact on reasoning capabilities. The findings highlight that while 4-bit quantization is safe and effective for production, 2-bit quantization often leads to incoherent outputs, calling for caution to avoid pitfalls when optimizing model performance.
Read the full blog for free on Medium.
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