Quantizing a local LLM silently destroys code generation quality with no way to measure the degradation

To fit a model in limited VRAM, you quantize it (Q4_K_M, Q5_K_M, Q8, GPTQ, AWQ). The quantized model generates syntactically valid code that looks correct but has subtle logic errors — off-by-one bugs, wrong comparison operators, swapped function arguments — at 2-3x the rate of the full-precision model. You cannot tell this is happening because the code compiles and often passes basic tests. So what? Developers pick a quantization level based on what fits in their VRAM, not based on quality metrics for their use case. A developer using Q4 quantization for a coding agent is shipping code with a hidden 2-3x higher defect rate and has no idea. They blame the model architecture when the real problem is quantization-induced degradation. Why does this persist in the first place? Every LLM benchmark (MMLU, HumanEval, MBPP) is run on full-precision models. When quantized benchmarks exist, they measure perplexity (a statistical metric) not task-specific quality like 'does the generated code actually work correctly in context.' There is no benchmark that measures 'Q4 of Model X produces working code Y% of the time vs full precision at Z%.' Users are flying blind — choosing between quantization levels by vibes and file size, not by measured quality for their specific task.