Sheilah Kirui explains the distinction between processing a prompt and generating output tokens, then describes how a smaller draft model proposes tokens for a larger target model to verify. The potential speedup comes with extra model and KV-cache memory, so the technique is not a free improvement for every deployment.
Sheilah Kirui profiles baseline and speculative vLLM servers on a Blackwell GPU. After resolving a live-demo interruption, she reports a roughly 1.6-times speedup for one structured-output example and contrasts token acceptance with a higher-temperature creative task. These are measurements from her demonstration, not a general performance guarantee.
Sheilah Kirui recommends considering draft-model size and compatibility, available VRAM, batch size, concurrency and the balance between input and output length. Long-context tasks with short responses may spend most of their time in prompt processing, which speculative decoding does not accelerate.
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