speculative-decoding

Speed up LLM inference without retraining by using speculative decoding and lookahead techniques to reduce latency. These methods parallelize autoregressive generation, enabling faster responses on existing base models.

Speculative decoding reduces LLM latency by using a fast draft model or internal heads to predict multiple future tokens in parallel. The base model verifies these predictions in a single forward pass, accelerating generation.

Lookahead decoding accelerates inference without additional draft-model training by using parallel generation strategies. It enables zero-training speedups for autoregressive LLMs, making it ideal for immediate deployment.

Speculative decoding works for real-time chat and code generation by increasing throughput and reducing latency in high-volume deployments. It is applicable wherever compute is constrained and rapid autoregressive responses are required.

The best way to increase LLM throughput is applying Medusa-style multi-head decoding or lookahead techniques. These approaches accelerate large model inference for high-volume deployments without requiring external training.

Medusa-style multi-head decoding and lookahead-based parallel generation require no external draft-model training. They rely on standard inference toolchains and a base model to deliver accelerated output.