This research investigates how low-bit quantization impacts mathematical reasoning abilities in large language models, addressing the tension between model efficiency and reasoning performance.

• Model quantization significantly reduces memory usage and computational costs
• Mathematical reasoning tasks are particularly vulnerable to quantization degradation
• Different quantization methods show varying impacts on reasoning capabilities
• Key finding: 4-bit quantization preserves most reasoning abilities while offering substantial efficiency gains

For engineering teams, this research provides critical insights into optimizing LLM deployment without sacrificing mathematical reasoning performance - essential for applications requiring computational thinking and problem-solving capabilities.

Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoning