Paying multi‐million‐dollar annual run‐rates to call giant models? Over the last 14 days the field has accelerated toward systematically distilling big models into compact specialists you can run cheaply on commodity hardware or on‐device, and this summary shows what’s changed and what to do. Recent preprints (2025‐10 to 2025‐12) and reproductions show 1–7B‐parameter students matching teachers on narrow domains while using 4–10× less memory and often 2–5× faster with under 5–10% loss; FinOps reports (through 2025‐11) flag multi‐million‐dollar inference costs; OEM benchmarks show sub‐3B models can hit interactive latency on devices with tens–low‐hundreds TOPS NPUs. Why it matters: lower cost, better latency, and privacy transform trading, biotech, and dev tools. Immediate moves: define task constraints (latency <50–100 ms, memory <1–2 GB), build distillation pipelines, centralize registries, and enforce monitoring/MBOMs.

Are you quietly burning multi‐million dollars a year on LLM inference while latency kills real‐time use cases? In the past 14 days (FinOps reports from 2025‐11–2025‐12), distillation, quantization, and edge NPUs have converged to make “right‐sized AI” the new priority — this summary tells you what that means and what to do. Big models (70B+) stay for research and synthetic data; teams are compressing them (7B→3B, 13B→1–2B) and keeping 90–95% task performance while slashing cost and latency. Quantization (int8/int4, GGUF) and device NPUs mean 1–3B‐parameter models can hit sub‐100 ms on phones and laptops. Impact: lower inference cost, on‐device privacy for trading and medical apps, and a shift to fleets of specialist models. Immediate moves: set latency/energy constraints, treat small models like APIs, harden evaluation and SBOMs, and close the distill→deploy→monitor loop.