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MEDUSA
Speed up LLM inference 2-3x with multiple decoding heads
Apache-2.0
ABOUT
Standard autoregressive LLM decoding generates one token at a time, making inference slow and memory-bound — especially for latency-sensitive applications like chatbots and real-time assistants. Medusa adds multiple lightweight decoding heads that predict several future tokens in parallel, combined with a tree-based attention mechanism that validates candidates efficiently. This speculative decoding approach achieves 2-3x wall-clock speedup without degrading output quality or requiring a separate draft model.
INSTALL
pip install medusa-llmINTEGRATION GUIDE
1. Accelerate chatbot inference to reduce perceived latency in interactive applications
2. Speed up batch LLM inference for document processing pipelines and bulk generation
3. Reduce per-token latency in real-time agentic workflows that require fast turn-around
4. Enable cost-effective LLM serving by processing more tokens per second on existing hardware
5. Integrate speculative decoding into existing inference pipelines with minimal code changes
TAGS
llminferencedecodingaccelerationpytorchspeculative-decoding