--- title: "Medusa vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fasterdecoding-medusa-vs-turboderp-exllama" tools: ["fasterdecoding-medusa", "turboderp-exllama"] --- # Medusa vs exllama *GraphCanon updated Aug 24, 2026* ## Verdict Pick Medusa if medusa enables quicker language model inference with parallel decoding strategies; pick exllama if exLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards. [Medusa](https://sites.google.com/view/medusa-llm) reports 2.8k GitHub stars, 205 forks, and 57 open issues, last pushed Jun 25, 2024. [exllama](https://github.com/turboderp/exllama) has 2.9k stars, 220 forks, and 65 open issues, last pushed Sep 30, 2023. Figures are from public GitHub metadata via [Medusa's repository](https://github.com/FasterDecoding/Medusa) and [exllama's repository](https://github.com/turboderp/exllama). | | [Medusa](/tools/fasterdecoding-medusa.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | Framework for accelerating LLM generation using multiple decoding heads | Memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 2,767 | 2,937 | | Forks | 205 | 220 | | Open issues | 57 | 65 | | Language | Jupyter Notebook | Python | | Adopt for | Medusa enables quicker language model inference with parallel decoding strategies. | ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Inference & Serving | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Medusa](/tools/fasterdecoding-medusa.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Days since push | 790d | 1041d | | Open issues (now) | 57 | 65 | | Stars delta | +9 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/fasterdecoding-medusa/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## Decision facts: Medusa - **Adopt for:** Medusa enables quicker language model inference with parallel decoding strategies. ## Decision facts: exllama - **Adopt for:** ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards. ## Choose when ### Choose Medusa if… - Medusa is primarily Jupyter Notebook; exllama is Python. - License: Medusa is Apache-2.0, exllama is MIT. - Tags unique to Medusa: acceleration, decoding, inference, llm. - When you need to accelerate inference times for large language models without compromising on output quality. ### Choose exllama if… - exllama is primarily Python; Medusa is Jupyter Notebook. - License: exllama is MIT, Medusa is Apache-2.0. - Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu. - Also covers LLM Frameworks. - exllama ships Docker support for self-hosted deployment. - - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ## When NOT to use Medusa - 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. ## When NOT to use exllama - - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts). ## Common questions ### What is the difference between Medusa and exllama? Medusa: Framework for accelerating LLM generation using multiple decoding heads. exllama: Memory-efficient rewrite of HF transformers for Llama with quantized weights. See the comparison table for live GitHub stats and shared categories. ### When should I choose Medusa over exllama? Choose Medusa over exllama when Medusa is primarily Jupyter Notebook; exllama is Python; License: Medusa is Apache-2.0, exllama is MIT; Tags unique to Medusa: acceleration, decoding, inference, llm; When you need to accelerate inference times for large language models without compromising on output quality. ### When should I choose exllama over Medusa? Choose exllama over Medusa when exllama is primarily Python; Medusa is Jupyter Notebook; License: exllama is MIT, Medusa is Apache-2.0; Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; Also covers LLM Frameworks; exllama ships Docker support for self-hosted deployment; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ### When should I avoid Medusa? 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. ### When should I avoid exllama? - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts). ### Is Medusa or exllama more popular on GitHub? exllama has more GitHub stars (2,937 vs 2,767). Stars measure visibility, not whether either tool fits your constraints. ### Are Medusa and exllama open source? Yes - both are open-source projects on GitHub (Medusa: Apache-2.0, exllama: MIT). ### Where can I find alternatives to Medusa or exllama? GraphCanon lists graph-backed alternatives at [Medusa alternatives](/tools/fasterdecoding-medusa/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([Medusa markdown twin](/tools/fasterdecoding-medusa/alternatives.md), [exllama markdown twin](/tools/turboderp-exllama/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/fasterdecoding-medusa-vs-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Medusa or exllama? Medusa: Dormant. exllama: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for Medusa and exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Medusa trust report](/tools/fasterdecoding-medusa/trust); [exllama trust report](/tools/turboderp-exllama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fasterdecoding-medusa`](/api/graphcanon/graph?tool=fasterdecoding-medusa) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_