Smaller Models, Unexpected Costs: Hidden Trade-offs in LLM Quantization for Automated Program Repair

arXiv 2606.27205·medium signal

Quantization shrinks an LLM's memory footprint, but this empirical study of LLM quantization for automated program repair shows benchmark scores mask changes in model behavior and non-functional overheads. The 'smaller and cheaper' framing hides costs that only show up when you measure behavior and runtime, not just accuracy. Useful for builders deploying quantized coding models who assume the only trade-off is a small accuracy drop.