Speculative decoding profiled on vLLM: 1.6x faster structured output on Blackwell
AI Engineer · youtube · 2026-10-07
Sheilah Kirui (Akamai developer advocate) explains when speculative decoding is worth enabling, profiling vLLM on a single NVIDIA Blackwell GPU.
- How it works: a small draft model proposes tokens, the target model verifies them in one forward pass, speeding up the decode phase
- The cost: hosting a second model and its KV cache in VRAM
- Choosing a draft model: size, shared tokenizer, cost vs accuracy
- Demo results: structured output ran 1.6x faster with high acceptance rate; creative writing saw far lower acceptance and gains
- When it's worth it: spare VRAM, small batches, structured output, short context; long-context and high-concurrency workloads benefit much less
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