speculative-decoding

To reduce LLM inference latency, you can apply speculative decoding, Medusa, and lookahead decoding techniques. These methods accelerate token generation by predicting and verifying multiple tokens in parallel, achieving 1.5-3.6x speedup for real-time applications.

The best way to speed up token generation on constrained hardware is using Jacobi iteration and tree-based attention. These approaches optimize parallel token generation strategies, enabling efficient LLM deployment without sacrificing output quality or requiring additional compute resources.

Speculative decoding maintains output quality by using draft models to propose tokens that are verified against the target LLM. This mechanism ensures the accelerated inference matches the original model's distribution while significantly reducing latency and improving throughput.

You can use Medusa decoding to accelerate inference on limited compute resources. Medusa uses multiple decoding heads to generate tokens in parallel, optimizing throughput and allowing efficient deployment of large language models on hardware with constrained environments.

Lookahead decoding uses Jacobi iteration for parallel token generation, whereas draft models rely on a smaller model proposing tokens for verification. Both inference optimization techniques reduce latency, but lookahead decoding avoids training a separate draft model.

You should avoid speculative decoding when the draft model's acceptance rate is low, causing verification overhead to negate the latency reduction benefits. If your application has strict memory constraints, maintaining additional draft models or multiple Medusa heads may be inefficient.