TriRoute lets LLMs cut inference cost by jointly routing attention, experts, and cache
TriRoute introduces a single lightweight controller that decides, for each token and layer, attention mode, expert subset, and KV‑cache bit‑width. By optimizing these three conditional‑compute axes together, it outperforms separate MoE, depth‑skipping, and cache‑quantization methods at equal FLOPs and memory while preserving performance on rare tokens.