Safety features occupy a low-dimensional subspace 10^2-10^3x more vulnerable to quantization noise than general perplexity metrics detect; Per-Channel Reduction (PCR) diagnoses failure modes and recovers up to 97% alignment with 35 GPU-minutes calibration.
Summary
Production LLM deployments use KV cache quantization to cut inference memory, but standard perplexity evals hide safety regression—Mistral-7B loses 15.2% of refusals at barely measurable perplexity cost. PCR gives you a diagnostic protocol to catch this before serving.
Why it matters
Production LLM deployments use KV cache quantization to cut inference memory, but standard perplexity evals hide safety regression—Mistral-7B loses 15.2% of refusals at barely measurable perplexity cost. PCR gives you a diagnostic protocol to catch this before serving.
Implementation verdict
Replaces blind quantization with mechanistic failure classification. Requires 20 calibration prompts, 35 GPU-minutes per model, and integration at quantization step—training-free. Ready now for production vLLM+FP8 stacks; validates on independent model families and production quantizers including KIVI.
Sources
Dev Signal
Get briefs like this in your inbox — free, every weekday.
100+ sources compressed into one 4-minute read. Ranked, cited, implementation-ready.