AQLM Quantization Matches Full Precision for Code Models, While QuIP# Reduces Correctness

Jul 17, 2026

A new empirical study evaluates six quantization methods on two large code model families, Qwen2.5-Coder and CodeLlama, using multilingual benchmarks. The results show that AQLM consistently matches or exceeds the full-precision baseline in functional correctness, while QuIP# leads to the largest degradation, especially on complex prompts. Security attributes remain stable across quantization methods, but robustness to prompt complexity varies. These findings offer practical guidance for deploying code models on resource-constrained hardware.

Why it matters: The study provides actionable insights for developers choosing quantization techniques to deploy large code models efficiently without sacrificing code correctness or security.

Full story at: arXiv Software Engineering ↗