Physics-Based Scheduling Cuts Edge LLM Energy Demands by 75%

QEIL v2 shifts edge-LLM optimization from static scaling rules to physics-grounded energy models, enabling 75% energy reduction and 38% latency improvement on handheld devices. This bridges a critical gap in on-device AI: as model sizes grow, battery life and thermal constraints become bottlenecks; dynamic physics-based scheduling adapts to actual device conditions in real time. For practitioners, the immediate value is clear—replacing hand-tuned heuristics with simulated annealing and semiconductor physics can unlock hours of extra battery life without sacrificing inference quality.