Is Speculative Decoding Worth It? Profiling vLLM on NVIDIA Blackwell — Akamai
Speculative decoding cuts LLM inference cost but only when GPU memory has spare capacity
An Akamai engineer explains speculative decoding — using a smaller draft model to speculatively generate tokens that a larger target model then validates in one pass — as a way to reduce inference latency and cost. The key practical finding is that it only pays off when GPU memory is underutilized; highly parallel, GPU-saturated workloads gain nothing. This is a useful operational heuristic for teams running self-hosted LLM inference at scale.