Smaller Models, Unexpected Costs: Trade-offs in LLM Quantization for Automated Program Repair
Large Language Models (LLMs) are powerful tools and have been increasingly adopted for complex software engineering tasks. As the number of parameters increases, results can often be improved, but this also imposes substantial memory requirements. While quantization effectively reduces the memory footprint, its overall impact is often summarized only by benchmark scores, which mask changes in model behavior and non-functional overheads. In this work, we conduct an empirical evaluation of LLM quantization using Automated Program Repair (APR), a complex task in software engineering. We analyze 13 quantization configurations spanning different bit-widths, methods, and target components (weights and KV-cache) across six representative LLMs, evaluated on two APR benchmarks (HumanEval-Java and Defects4J). Our findings reveal that base and quantized models can provide different sets of repaired problems with little overlap, while retaining a comparable number of repaired problems. Although quantization successfully reduces memory footprints by up to 85%, it increases both inference time and energy consumption, which we attribute to suboptimal hardware utilization. Our Pareto trade-off analysis shows that 48% of the configurations evaluated are strictly dominated by alternatives. Rather than identifying a superior quantization method, our findings highlight that the trade-offs between effectiveness, memory footprint, and energy efficiency are sensitive to the underlying model architecture and the complexity of the task.