Speculative Decoding

2026-06-02 · Source: Anup Jadhav · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, short

Summary

Speculative decoding is an inference optimization technique designed to accelerate large language model (LLM) generation by reducing expensive sequential decoding steps. It operates on a "draft first, verify second" principle, employing a fast, small "draft model" to propose several future tokens. A larger, more expensive "target model" then verifies these proposed tokens in a single parallel pass. If the target model agrees with the draft, those tokens are accepted all at once; otherwise, generation falls back to the target model's preferred token at the point of disagreement. This method aims to achieve the same output quality as the target model but with significantly fewer calls, improving latency and throughput. The speedup is maximized when the draft model is cheaper, well-aligned with the target model, and many next tokens are predictable, particularly for non-creative tasks. Crucially, the algorithm ensures the final output distribution remains equivalent to sampling directly from the large target model, preserving correctness while enhancing speed.

Key takeaway

For AI Engineers optimizing LLM inference, implementing speculative decoding can significantly reduce latency and improve throughput without altering the target model's output distribution. You should consider this technique for applications involving predictable text generation, leveraging a smaller, well-aligned draft model to accelerate expensive sequential decoding steps. Be aware that its benefits diminish for highly creative tasks or when the draft model frequently mispredicts.

Key insights

Speculative decoding accelerates LLM inference by using a fast draft model to propose tokens and a large target model to verify them in parallel.

Principles

• "Draft first, verify second" for LLM inference.
• Target model maintains output distribution authority.
• Speedup depends on draft model alignment and predictability.

Method

A small draft model proposes k tokens; the large target model scores them in parallel. The accepted prefix is kept, and the first rejected token is replaced by the target model's choice.

In practice

• Use smaller, aligned models for drafting.
• Prioritize for predictable text generation.
• Optimize verification for parallel processing.

Topics

• Speculative Decoding
• LLM Inference Optimization
• Parallel Decoding
• Draft Models
• Target Models
• Latency Reduction

Best for: Machine Learning Engineer, AI Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Anup Jadhav.