Quantize with Confidence? An Empirical Study of Quantization for Code Generation
The growing adoption of local inference frameworks such as Ollama has made it increasingly common for developers to run large code models on laptops and other resource-constrained hardware. In these settings, post-training quantization is essential for reducing memory footprint and enabling practical deployment, yet its impact on generated code remains insufficiently understood. We empirically evaluate six state-of-the-art quantization methods (GPTQ, AWQ, QuIP#, AQLM, BitsAndBytes, and GGUF) on two representative large code model families, Qwen2.5-Coder and CodeLlama, using the multilingual McEval and CoderEval benchmarks for Python and Java. We assess functional correctness (pass@1) toge...
| Authors | Saima Afrin, Md. Zahidul Haque, Antonio Mastropaolo |
|---|---|
| Primary category | cs.SE |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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