Speculative Decoding in Production: Free Tokens and Hidden Traps
Most LLM inference bottlenecks come down to one uncomfortable fact: the GPU is waiting on memory bandwidth, not compute. Each token generated requires loading the entire model's weights from HBM, and that transfer dominates runtime. Speculative decoding was designed to exploit this gap — but the gains depend on conditions your benchmark almost certainly didn't test.
Teams that ship speculative decoding into production often see it underperform lab numbers by 40–60%. Not because the technique is flawed, but because the workload characteristics differ in ways that matter: larger batch sizes, shorter outputs, stricter output constraints. Understanding when speculative decoding actually helps — and when it silently hurts — is the prerequisite for deploying it responsibly.
