Physics‑based adaptation slashes edge LLM energy

The conventional view holds that edge‑LLM runtimes are limited by static, rule‑of‑thumb scaling of compute and memory, leaving most of the device’s power budget unused. QEIL v2 overturns that assumption by grounding its resource allocator in a physics‑derived energy model and steering the search with simulated‑annealing, delivering a dramatic cut in inference energy. Earlier work, such as QEIL v1, relied on fixed efficiency factors and greedy heuristics, which yielded modest speedups but still...