speculative decoding

Verify a drafted prefix without biasing the target distribution, then roll back rejected KV state. A six-token prefix exposes the off-by-one: nine committed tokens can have only eight valid KV entries.

Trace an accepted prefix, a rejection and the correction token; derive emitted progress and compare it with a measured round cost.

Compare proposal methods by committed output over complete round time, with explicit sampling guarantees and target compatibility.

Use the actual tensor index to size Kimi K3, then separate a published hardware starting point from production capacity. Account for mixed precision, hybrid recurrent state, parser behavior, and the workload behind a throughput claim.

Check the bound’s assumptions, locate the critical path, and quantify which optimization can actually improve one user’s decode latency.

Compare committed output per complete draft-and-verify cycle with ordinary decoding. Derive the acceptance-length model, separate it from engine counters, and test latency, goodput and memory across the actual workload.

Compare the output of a speculative round with drafting and verification cost. Work the break-even condition, then design a measured rollout with memory and correctness checks.

Derive and simulate speculative output counts, then compare them with explicitly assumed or measured round costs.