--- title: "mlx-serve vs ollama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ddalcu-mlx-serve-vs-ollama-ollama" tools: ["ddalcu-mlx-serve", "ollama-ollama"] --- # mlx-serve vs ollama *GraphCanon updated Jul 15, 2026* ## Verdict Pick mlx-serve when mlx-serve is primarily Zig; ollama is Go; pick ollama when ollama is primarily Go; mlx-serve is Zig. [mlx-serve](http://mlxserve.com/) reports 283 GitHub stars, 22 forks, and 3 open issues, last pushed Jul 14, 2026. [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 [mlx-serve's repository](https://github.com/ddalcu/mlx-serve) and [ollama's repository](https://github.com/ollama/ollama). | | [mlx-serve](/tools/ddalcu-mlx-serve.md) | [ollama](/tools/ollama-ollama.md) | | --- | --- | --- | | Tagline | Native LLM inference server for Apple Silicon. OpenAI + Anthropic API compatible. No Python. Includes MLX Core macOS app with chat, agent mode, and tool calling. | Get up and running with various large language models using Ollama. | | Stars | 283 | 175,936 | | Forks | 22 | 16,939 | | Open issues | 3 | 3,423 | | Language | Zig | 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 | MIT | MIT license - permissive open-source licensing that allows for broad use of the tool. | | Categories | AI Agents, Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mlx-serve](/tools/ddalcu-mlx-serve.md) | [ollama](/tools/ollama-ollama.md) | | --- | --- | --- | | Days since push | 0d | 1d | | Open issues (now) | 3 | 3.4k | | Owner type | User | Organization | | Full report | [trust report](/tools/ddalcu-mlx-serve/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 mlx-serve if… - mlx-serve is primarily Zig; ollama is Go. - Tags unique to mlx-serve: agent, anthropic-api, apple-silicon, claude code. - Also covers AI Agents. ### Choose ollama if… - ollama is primarily Go; mlx-serve is Zig. - 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 mlx-serve - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - 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 mlx-serve and ollama? mlx-serve: Native LLM inference server for Apple Silicon. OpenAI + Anthropic API compatible. No Python. Includes MLX Core macOS app with chat, agent mode, and tool calling.. 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 mlx-serve over ollama? Choose mlx-serve over ollama when mlx-serve is primarily Zig; ollama is Go; Tags unique to mlx-serve: agent, anthropic-api, apple-silicon, claude code; Also covers AI Agents. ### When should I choose ollama over mlx-serve? Choose ollama over mlx-serve when ollama is primarily Go; mlx-serve is Zig; 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 mlx-serve? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. 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 mlx-serve or ollama more popular on GitHub? ollama has more GitHub stars (175,936 vs 283). Stars measure visibility, not whether either tool fits your constraints. ### Are mlx-serve and ollama open source? Yes - both are open-source projects on GitHub (mlx-serve: MIT, ollama: MIT). ### Where can I find alternatives to mlx-serve or ollama? GraphCanon lists graph-backed alternatives at [mlx-serve alternatives](/tools/ddalcu-mlx-serve/alternatives) and [ollama alternatives](/tools/ollama-ollama/alternatives) ([mlx-serve markdown twin](/tools/ddalcu-mlx-serve/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/ddalcu-mlx-serve-vs-ollama-ollama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mlx-serve or ollama? mlx-serve: Very active. 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 mlx-serve and ollama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mlx-serve trust report](/tools/ddalcu-mlx-serve/trust); [ollama trust report](/tools/ollama-ollama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ddalcu-mlx-serve`](/api/graphcanon/graph?tool=ddalcu-mlx-serve) - 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/_