Medusa accelerates LLM inference by appending additional decoding heads that predict multiple future tokens in parallel. Unlike speculative decoding, it does not require a separate draft model. The original model remains unchanged while only the new heads are fine-tuned. During generation, tree-based attention combines candidates from all heads, and an acceptance scheme selects the longest plausible prefix for continued decoding.