Speculative Decoding
Definition
Speculative decoding accelerates output by fast proposals that the target model jointly verifies.
Speculative decoding lets a faster draft model propose multiple tokens and the target model jointly evaluate a proposal block. A correct acceptance and correction procedure preserves the target distribution.
Simply explained
A fast helper proposes several next text segments, which the actual model then jointly checks.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
An assistant uses a small draft model to reduce the main model's response latency.
Limits and pitfalls
- Claiming distribution equivalence without a correct acceptance procedure, incompatibly combining draft and target tokenization, or generalizing acceleration without acceptance rate, hardware, and additional effort.
Origin & history
Leviathan and co-authors as well as Chen and co-authors published related methods for accelerated autoregressive inference in 2023.
Use in mid-sized companies
- Possible application: An assistant uses a small draft model to reduce the main model's response latency.
Frequently asked questions
What does speculative decoding mean?
Speculative decoding lets a faster draft model propose multiple tokens and the target model jointly evaluate a proposal block. A correct acceptance and correction procedure preserves the target distribution. What mistakes should be avoided? Claiming distribution equivalence without a correct acceptance procedure, incompatibly combining draft and target tokenization, or generalizing acceleration without acceptance rate, hardware, and additional effort.
Sources
- Fast Inference from Transformers via Speculative Decoding · Leviathan et al., Fast Inference from Transformers via Speculative Decoding
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: