Is Speculative Decoding Worth It? Profiling vLLM on NVIDIA Blackwell — Akamai

This post contributes to the understanding of speculative inference by profiling its performance on NVIDIA Blackwell GPUs using vLLM. It demonstrates that speculative decoding can yield significant speedups (e.g., 1.6x for structured output) when the draft model has a high acceptance rate and the workload is suitable (small batches, short context, spare VRAM). The analysis also quantifies the memory cost of hosting a second model and its KV cache, and shows that creative writing tasks and long-context workloads benefit less due to lower acceptance rates and increased KV cache demands, respectively.