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title: "Why is Speculative Decoding Fast? (4 minute read)"
description: "Joe Barrow’s explainer argues that speculative decoding can accelerate low-batch inference not by reducing total computation, but by changing how available GPU compute is used."
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# Why is Speculative Decoding Fast? (4 minute read)

## Retrieval answer

Joe Barrow’s explainer argues that speculative decoding can accelerate low-batch inference not by reducing total computation, but by changing how available GPU compute is used.

Joe Barrow’s explainer argues that speculative decoding can accelerate low-batch inference not by reducing total computation, but by changing how available GPU compute is used. A small draft model proposes several tokens; the larger model verifies them in parallel in one forward pass. The article says single-token decoding is often memory-bound, leaving compute underused, while speculative verification can use that slack to advance one sequence faster. At higher throughput, speculation may waste compute, particularly when proposed tokens are rejected. Barrow notes that vLLM offers a batch-size setting to disable speculative decoding. The piece describes the mechanism and tradeoff; it does not provide a general performance guarantee or benchmark results. Source: https://jbarrow.ai/2026-10-08-why-is-speculative-decoding-fast

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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