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Medusa

Medusa is an open-source framework for accelerating large language model text generation with multiple decoding heads. The heads predict likely future tokens, which are combined through tree-based attention and an acceptance step. The project documents command-line inference and recipes for training Medusa heads, full-model training, and self-distillation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Medusa — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Medusa when you want to test multi-head decoding for LLM generation or follow its documented inference and training workflows, particularly for single-GPU, batch-size-1 local hosting.

Who it is for

  • LLM developers exploring multi-head decoding and inference acceleration.
  • Practitioners running supported Medusa model weights in a documented single-GPU, batch-size-1 setup.
  • Researchers or developers investigating Medusa-1 or Medusa-2 training and self-distillation workflows.

Documented uses

  • Run command-line inference with documented Medusa model weights.
  • Load a base model in 8-bit or 4-bit format for inference.
  • Explore training added decoding heads or the Medusa-2 full-model recipe.
  • Investigate self-distillation for adding Medusa to a fine-tuned LLM without its original training data.
Before you choose

Requirements & limits

The repository is public and identifies an Apache-2.0 license.

  • The documented inference configuration is single GPU with batch size 1.
  • The repository lists Medusa-1 and Medusa-2 weights for specific Vicuna and Zephyr model variants.
  • Inference supports optional 8-bit or 4-bit base-model loading and a base-model path override.
  • The README reports speedups for particular configurations; results may differ across models and workloads.
  • Training documentation distinguishes the initial Medusa-1 example from the updated Axolotl-based workflow.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Launch inference with a Medusa model

Install Medusa and start its documented command-line inference interface with a Medusa model.

Before you start

  • A Medusa model or its path.
  • A single GPU; the documented inference setup uses batch size 1.

Steps

  1. Install from the repository with the command below, then enter the directory and run the command below.

    Documented command
    git clone https://github.com/FasterDecoding/Medusa.git
    Documented command
    pip install -e .

  2. Launch the CLI with CUDA_VISIBLE_DEVICES=0 python -m medusa.inference.cli --model [path of medusa model].

  3. Optionally add --load-in-8bit or --load-in-4bit; use --base-model [path of base model] to override the base-model location.

Expected outcome

The Medusa command-line inference interface is launched for the selected model.

Guide limitations

  • The documented inference scope is single-GPU with batch size 1.
  • The repository notes that the pip installation may not be the latest version.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
MedusaRun command-line inference with documented Medusa model weights.The repository is public and identifies an Apache-2.0 license.
Cloud AI SDKAdapt and run documented model recipes for computer-vision and NLP inference.Cloud, ServerThe GitHub repository is public. Its public visibility does not establish license terms.
TurboquantReplace a model’s existing inference cache with a compressed cache.Cloud, Linux, macOS, Self-hostedThe project describes itself as open source. The README documents installation through pip or from a source checkout.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
FasterDecoding/Medusa · Official homepage title · retained in its source language
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