speculative-decoding

Speculative decoding accelerates LLM inference by using a small draft model to propose tokens while a larger target model verifies them, achieving 1.5–3.6× latency reductions for real-time chat. It preserves output quality while enabling parallel generation.

Medusa-style multiple decoding heads predict future tokens directly without needing an external draft model. They attach to the target model to generate candidate tokens in parallel, streamlining the speculative decoding process for faster LLM speedup.

Lookahead decoding uses Jacobi iteration to generate parallel n-grams and verify them for consistency. This speculative decoding technique accelerates LLM inference by extracting and validating multiple token sequences simultaneously without a draft model.

Yes, speculative decoding supports production deployment patterns including integration with vLLM. This combination enables parallel generation strategies optimized for real-time workloads, delivering significant latency speedups in interactive assistants and code generation.

Speculative decoding reduces inference latency by combining fast draft-model speculation with a validating target model. This approach dramatically reduces latency for interactive applications while fully preserving the output quality of the larger target model.

Use a draft model when you have a compatible small model to propose tokens for the larger target model. Use Medusa-style multiple decoding heads when you want to predict future tokens directly without managing an external draft model.