This training-free trick speeds up MoE serving up to 2.45x with no quality loss
MoE serving has two bottlenecks with opposite fixes: prefill is compute-bound on per-token experts, decode is memory-bound on the batch's expert set. Prior training-free accelerators optimize only one, and throw away the skipped experts' output. ExFold treats both phases as one budgeted approximation problem, using calibrated scalar projectors to fold excluded experts' contributions into the retained ones. In a vLLM plugin it delivers up to 1.41x TTFT and 2.45x TPOT speedups while keeping ~99% of quality.