--- title: "Medusa vs ollama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fasterdecoding-medusa-vs-ollama-ollama" tools: ["fasterdecoding-medusa", "ollama-ollama"] --- # Medusa vs ollama *GraphCanon updated Jul 11, 2026* ## Verdict Pick Medusa when medusa is primarily Jupyter Notebook; ollama is Go; pick ollama when ollama is primarily Go; Medusa is Jupyter Notebook. [Medusa](https://sites.google.com/view/medusa-llm) reports 2.8k GitHub stars, 204 forks, and 57 open issues, last pushed Jun 25, 2024. [ollama](https://ollama.com) has 176k stars, 17k forks, and 3.4k open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [Medusa's repository](https://github.com/FasterDecoding/Medusa) and [ollama's repository](https://github.com/ollama/ollama). | | [Medusa](/tools/fasterdecoding-medusa.md) | [ollama](/tools/ollama-ollama.md) | | --- | --- | --- | | Tagline | Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads | Get up and running with various large language models using Ollama. | | Stars | 2,755 | 175,936 | | Forks | 204 | 16,939 | | Open issues | 57 | 3,423 | | Language | Jupyter Notebook | Go | | Adopt for | - | Ollama is a Go-based platform that provides tools for deploying and managing large language models (LLMs) like Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma using docker images, package managers, cloud and | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT license - permissive open-source licensing that allows for broad use of the tool. | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Medusa](/tools/fasterdecoding-medusa.md) | [ollama](/tools/ollama-ollama.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 745d | 1d | | Open issues (now) | 57 | 3.4k | | Security scan | No lockfile | 52 low (52 low) | | Full report | [trust report](/tools/fasterdecoding-medusa/trust.md) | [trust report](/tools/ollama-ollama/trust.md) | ## Decision facts: ollama - **Hosting:** self hosted - Ollama supports self-hosted and cloud-deployable models using Docker, Helm charts, and various package managers. - **Adopt for:** Ollama is a Go-based platform that provides tools for deploying and managing large language models (LLMs) like Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma using docker images, package managers, cloud and - **License detail:** MIT license - permissive open-source licensing that allows for broad use of the tool. ## Choose when ### Choose Medusa if… - Medusa is primarily Jupyter Notebook; ollama is Go. - License: Medusa is Apache-2.0, ollama is MIT. - Tags unique to Medusa: jupyter notebook, llm, llm-inference. ### Choose ollama if… - ollama is primarily Go; Medusa is Jupyter Notebook. - License: ollama is MIT, Medusa is Apache-2.0. - Ollama supports self-hosted and cloud-deployable models using Docker, Helm charts, and various package managers. - Tags unique to ollama: deepseek, gemma, glm, go. - ollama ships Docker support for self-hosted deployment. - Use Ollama when you require a multi-model platform supporting several large language models such as Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and intend to deploy in various cloud or ## When NOT to use Medusa - Last GitHub push was 746 days ago (dormant maintenance, Jun 25, 2024). Validate activity before betting a new project on Medusa. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use ollama - Avoid using Ollama if you are only interested in a single LLM deployment and seek simplified, model-specific solutions with tailored support rather than a comprehensive multi-model platform. ## Common questions ### What is the difference between Medusa and ollama? Medusa: Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads. ollama: Get up and running with various large language models using Ollama.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Medusa over ollama? Choose Medusa over ollama when Medusa is primarily Jupyter Notebook; ollama is Go; License: Medusa is Apache-2.0, ollama is MIT; Tags unique to Medusa: jupyter notebook, llm, llm-inference. ### When should I choose ollama over Medusa? Choose ollama over Medusa when ollama is primarily Go; Medusa is Jupyter Notebook; License: ollama is MIT, Medusa is Apache-2.0; Ollama supports self-hosted and cloud-deployable models using Docker, Helm charts, and various package managers; Tags unique to ollama: deepseek, gemma, glm, go; ollama ships Docker support for self-hosted deployment; Use Ollama when you require a multi-model platform supporting several large language models such as Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and intend to deploy in various cloud or. ### When should I avoid Medusa? Last GitHub push was 746 days ago (dormant maintenance, Jun 25, 2024). Validate activity before betting a new project on Medusa. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid ollama? Avoid using Ollama if you are only interested in a single LLM deployment and seek simplified, model-specific solutions with tailored support rather than a comprehensive multi-model platform. ### Is Medusa or ollama more popular on GitHub? ollama has more GitHub stars (175,936 vs 2,755). Stars measure visibility, not whether either tool fits your constraints. ### Are Medusa and ollama open source? Yes - both are open-source projects on GitHub (Medusa: Apache-2.0, ollama: MIT). ### Where can I find alternatives to Medusa or ollama? GraphCanon lists graph-backed alternatives at [Medusa alternatives](/tools/fasterdecoding-medusa/alternatives) and [ollama alternatives](/tools/ollama-ollama/alternatives) ([Medusa markdown twin](/tools/fasterdecoding-medusa/alternatives.md), [ollama markdown twin](/tools/ollama-ollama/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-ollama-ollama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Medusa or ollama? Medusa: Dormant. ollama: Very active. 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 ollama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Medusa trust report](/tools/fasterdecoding-medusa/trust); [ollama trust report](/tools/ollama-ollama/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/_