LLM inference efficiency via adaptive routing, pruning, and hardware‑aware scaling Dynamic routing that selects full or sparse attention per layer cuts the cost of long‑context processing. Flux Attention implements this routing and delivers 2–3× speedups on benchmark tasks while keeping accuracy within a few points [1] . When routing is paired with token‑level pruning, the gains multiply. A task‑conditioned pruning network discards 92 % of input tokens that are irrelevant for the next action, yet it preserves recall and F1 scores [2] . Both techniques are hardware‑aware: QEIL v2 replaces hand‑tuned heuristics with a physics‑based metric and a simulated‑annealing optimizer. On an 8B model the optimizer lowers inference energy by 75.6 % and latency by 38.3 % [3] . Why it matters: inference cost dominates deployment budgets for large models. The three papers together show a practical path to halve compute, cut energy, and still run demanding long‑context applications.…