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Decision brief
Medusa enables quicker language model inference with parallel decoding strategies.
Good fit when
- When you need to accelerate inference times for large language models without compromising on output quality.
- For applications requiring real-time interaction where rapid response times are critical and multiple decoding paths can be utilized.
Avoid when
- If your model does not benefit from parallelized decoding, such as when the model architecture inherently limits parallel execution efficiency.
- In scenarios where the computational resources required for multiple decoding heads exceed what is available or cost-effective within your infrastructure.
Observed Jul 14, 2026 · Source: enrich:decision_facts
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Maintenance and security
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Install
git clone https://github.com/FasterDecoding/MedusaSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Medusa is designed to speed up the inference process of large language models through parallelized decoding strategies.
Capability facts
- Languages
- jupyter notebook, python
Source: github.language+pyproject.toml · Aug 24, 2026
Categories
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README
Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads
News 🔥
- [2024/1] Medusa technical report is now available on arXiv. We've added multiple new features, including Medusa-2 recipe for full-model training, self-distillation for adding Medusa to any fine-tuned LLM, etc. The new results show a 2.2-3.6x speedup over the original model on a range of LLMs.
Introduction
Medusa is a simple framework that democratizes the acceleration techniques for LLM generation with multiple decoding heads.
We aim to tackle the three pain points of popular acceleration techniques like speculative decoding:
- Requirement of a good draft model.
- System complexity.
- Inefficiency when using sampling-based generation.
We aim to solve the challenges associated with speculative decoding by implementing the following ideas:
- Instead of introducing a new model, we train multiple decoding heads on the same model.
- The training is parameter-efficient so that even the "GPU-Poor" can do it. And since there is no additional model, there is no need to adjust the distributed computing setup.
- Relaxing the requirement of matching the distribution of the original model makes the non-greedy generation even faster than greedy decoding.
In the initial release, our primary focus is on optimizing Medusa for a batch size of 1—a setting commonly utilized for local model hosting. In this configuration, Medusa delivers approximately a 2x speed increase across a range of Vicuna models. We are actively working to extend Medusa's capabilities by integrating it into additional inference frameworks, with the aim of achieving even greater performance gains and extending Medusa to broader settings.
In the updated version, we add support for full-model training, called Medusa-2 (compared to Medusa-1, which only trains the new heads), which requires a special recipe that adds the speculative prediction ability while keeping the original model's performance.
We also add support for self-distillation, which allows us to add Medusa to any fine-tuned LLM without requiring the availability of the original training data.
Contents
- Introduction
- Contents
- Installation
- Method 1: With pip (may not be the latest version)
- Method 2: From the source (recommended)
- Model Weights
- Inference
- Training
- Training (legacy)
- Push to Hugging Face Hub
- [Citation
For agents
This page has a .md twin and JSON over the API.