A volunteer red team filed 4,962 AI findings across 390 repositories in 27.5 hours, and not one came from a public US frontier model. Not a capability gap: the capable versions sit behind identity gates. Refusal policy is now where the labs differentiate, and it belongs on your dependency list.
Knowledge distillation is a large model teaching a small one, and it works almost exactly like school: soft labels instead of a bare answer key, pairing weeks instead of reports, and a tutor who marks your own attempt rather than a textbook of last year’s solutions. The limits map too, and the last one costs money when you size the hardware.
Public frontier models lean progressive on open benchmarks. Local inference via llama.cpp is still the practical way to keep an uncensored second opinion. Memory is the bottleneck; TurboQuant-style KV compression is what will make local writing workers routine in multi-model agents.
A small coding model mocked on r/LocalLLM is actually a clean case of execution-verified distillation. Here is how the method works, when distilling a frontier model into a cheap local one pays off, and the three things the hype leaves out.