Quantize with Confidence? An Empirical Study of Quantization for Code Generation

Research Overview

As developers increasingly deploy large code models (LCMs) on local, resource-constrained hardware, post-training quantization has become a standard strategy to reduce memory footprints. However, because quantization is a lossy compression process, it can impact the model's ability to reason over complex code syntax and semantics. This study provides a comprehensive empirical evaluation of six state-of-the-art 4-bit quantization methods—GPTQ, AWQ, QuIP#, AQLM, BitsAndBytes, and GGUF—across two major model families, Qwen2.5-Coder and CodeLlama.

Methodology

The researchers evaluated these models using the McEval, CoderEval, and BigCodeBench benchmarks, covering both Python and Java. The study assessed two primary dimensions: functional correctness (pass@1) and code quality (maintainability, reliability, security, and structural complexity). A unique contribution of this work is the analysis of model robustness under varying prompt complexity, measured by token length and Shannon entropy, to determine if certain quantization methods struggle more with complex, high-information-density inputs.

Key Findings

The study reveals that while 4-bit quantization is generally viable, the choice of technique is critical. AQLM emerged as the most robust method, often performing on par with or better than the full-precision baseline. Conversely, QuIP# showed the most significant degradation in functional correctness, particularly when handling complex prompts. While security attributes remained stable across all tested methods, other quality metrics varied: BitsAndBytes showed higher degradation in Python complexity metrics, and AWQ was linked to increased maintainability issues in Java. The results suggest that practitioners should look beyond simple pass@1 metrics when selecting a quantization strategy for production environments.

This research provides actionable guidance for developers who need to balance model performance with hardware limitations. By demonstrating that quantization effects are not uniform across techniques or prompt types, the authors highlight the necessity of rigorous, multi-dimensional evaluation for quantized models. This ensures that the code generated by local, compressed models remains reliable and maintainable for real-world software engineering tasks.