ModelRefs / Lookahead Decoding — AI Glossary
Lookahead Decoding — AI Glossary
A parallel decoding strategy that generates and verifies token n-grams in parallel without a separate draft model. Achieved 2–4× speedup on LLaMA-2 chat.
Overview
Lookahead decoding (Fu et al. 2023) maintains a Jacobi decoding window, iteratively refining multiple token positions simultaneously. Unlike speculative decoding it needs no separate model. Achieved 2–4× speedup on LLaMA-2 chat. Useful when model size precludes finding a suitable draft model.
Reference details
| Topic | inference |
|---|---|
| Also known as | Jacobi decoding |
| Last reviewed | 2026-06-24 |
Related terms
Commonly confused with
Like speculative decoding, this speeds up generation without changing the output distribution. The difference is what it needs: speculation requires a separate small draft model with a matching tokenizer, which you must host and keep aligned; lookahead generates and verifies candidate n-grams using the same model, so there is no second model to serve and no acceptance rate that depends on model-pair quality.
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Frequently asked questions
What is Lookahead Decoding?
A parallel decoding strategy that generates and verifies token n-grams in parallel without a separate draft model.
Is Lookahead Decoding the same as Jacobi decoding?
Yes — Jacobi decoding are common aliases for Lookahead Decoding.
What concepts are related to Lookahead Decoding?
Closely related concepts include speculative decoding, draft model, throughput.