Why it matters
This vLLM walkthrough explains the tradeoff behind draft-model speculative decoding: a smaller model proposes tokens and the target model verifies them, adding draft computation and memory in exchange for possible faster generation. Acceptance rate, prompt length, output length, sampling settings, and concurrency all affect the result. The talk includes a small live comparison, while vLLM documentation limits the technique’s expected benefit to suitable workloads and distinguishes distributional correctness from identical outputs across execution configurations. Its reported demo speedup is not a transferable benchmark, and spare GPU memory alone does not establish a performance benefit.
My takeaway: Benchmark the baseline and speculative configuration separately on the same hardware and request mix. Record time to first token, output latency, throughput, acceptance rate, memory, and correctness at several concurrency levels; retain speculation only where the measured tradeoff helps.