Quantized model files silently degrade in quality with no way to verify or audit them

Summary

Local LLM users download a specific quantization (e.g., Q4_K_M GGUF of a popular model) that initially performs brilliantly, then later find the same file or a re-download of the 'same' quant failing basic tasks, forgetting instructions, and refusing to finish complex work. The user in this case has 1,487 upvotes on a rant accusing the model publisher of secretly 'nerfing' the quant so the next model looks better — and crucially, they have no evidence either way. This is the dark side of the quantization decision problem: users can research which bit-width fits their VRAM, but once downloaded they have no way to (a) verify that a community re-upload or new revision of the same quant label actually matches the original quality, (b) detect silent quality regressions after a runtime/llama.cpp update or a changed KV-cache/context setting, or (c) distinguish 'the quant is bad' from 'my prompt, my context length, or my sampler is causing this.' Every existing hub ranks downloads by popularity and every benchmark reports aggregate scores for a model family, not the quality of a specific quant artifact under a specific runtime and context configuration. So users burn hours re-testing, re-downloading tens of GB, second-guessing their own setup, and end up distrusting the whole quantization ecosystem — a high-emotion, high-cost, genuinely unsolved gap that a VRAM-and-quantization recommender is uniquely positioned to close by fingerprinting, version-pinning, and continuously re-validating specific quant artifacts against reference quality and long-context behavior.