--- title: "airunner vs afm-Server" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/capsize-games-airunner-vs-techopolis-afm-server" tools: ["capsize-games-airunner", "techopolis-afm-server"] --- # airunner vs afm-Server *GraphCanon updated Aug 13, 2026* ## Verdict Pick airunner if aIRunner supports offline multimodal operations with a strong focus on image generation, real-time voice conversations, and LLM-powered chatbots via GUI or API; pick afm-Server if afm-Server provides macOS users with local access to Apple's on-device foundational AI models through an API compatible with OpenAI standards. [airunner](https://airunner.capsizegames.com) reports 1.3k GitHub stars, 100 forks, and 5 open issues, last pushed Jul 8, 2026. [afm-Server](https://github.com/Techopolis-Online/Perspective-Intelligence) has 189 stars, 8 forks, and 1 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [airunner's repository](https://github.com/Capsize-Games/airunner) and [afm-Server's repository](https://github.com/Techopolis/afm-Server). | | [airunner](/tools/capsize-games-airunner.md) | [afm-Server](/tools/techopolis-afm-server.md) | | --- | --- | --- | | Tagline | Offline inference engine for art, real-time voice conversations, LLM powered chatbots and automated workflows | macOS menu bar app for exposing Apple's on-device Foundation Models via an OpenAI-compatible API | | Stars | 1,312 | 189 | | Forks | 100 | 8 | | Open issues | 5 | 1 | | Language | Python | Swift | | Adopt for | AIRunner supports offline multimodal operations with a strong focus on image generation, real-time voice conversations, and LLM-powered chatbots via GUI or API. | afm-Server provides macOS users with local access to Apple's on-device foundational AI models through an API compatible with OpenAI standards. | | Persona | - | - | | Runtime | - | - | | License | GPL-3.0, ensuring free use but requiring sharing of modifications in a similar manner. | MIT | | Categories | Computer Vision, Inference & Serving, Speech & Audio | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [airunner](/tools/capsize-games-airunner.md) | [afm-Server](/tools/techopolis-afm-server.md) | | --- | --- | --- | | Maintenance | Active (82%) | Steady (60%) | | Days since push | 21d | 72d | | Open issues (now) | 5 | 1 | | Full report | [trust report](/tools/capsize-games-airunner/trust.md) | [trust report](/tools/techopolis-afm-server/trust.md) | ## Decision facts: airunner - **Requirements:** Min 16 GB RAM; Requires Docker; Requires specific GPU support (NVIDIA) and larger storage allocations compared to competitors - **Adopt for:** AIRunner supports offline multimodal operations with a strong focus on image generation, real-time voice conversations, and LLM-powered chatbots via GUI or API. - **License detail:** GPL-3.0, ensuring free use but requiring sharing of modifications in a similar manner. ## Decision facts: afm-Server - **Adopt for:** afm-Server provides macOS users with local access to Apple's on-device foundational AI models through an API compatible with OpenAI standards. ## Choose when ### Choose airunner if… - airunner is primarily Python; afm-Server is Swift. - License: airunner is GPL-3.0, afm-Server is MIT. - Requirements: Min 16 GB RAM; Requires Docker; Requires specific GPU support (NVIDIA) and larger storage allocations compared to competitors. - Tags unique to airunner: ai-art, chatbot, image-generation, speech-to-text. - Also covers Computer Vision, Speech & Audio. - airunner ships Docker support for self-hosted deployment. - When needing an all-inclusive offline tool for both image generation and speech-to-text/text-to-speech functionalities ### Choose afm-Server if… - afm-Server is primarily Swift; airunner is Python. - License: afm-Server is MIT, airunner is GPL-3.0. - Tags unique to afm-Server: apple-intelligence, foundation-models, local-llm, macos. - When you need local, cloud-free inference services from Apple's device-based AI models and are working within a macOS environment ## When NOT to use airunner - If your primary need is cloud-based services as AIRunner focuses on local deployments only - In scenarios where minimal hardware requirements are crucial, given AIRunner's higher system demands (min. 16 GB RAM, NVIDIA RTX 3060) ## When NOT to use afm-Server - In scenarios where a cross-platform solution is necessary as afm-Server only supports macOS environments - When your application demands real-time, high-throughput API access that can be limited by the device's hardware capabilities compared to cloud solutions ## Common questions ### What is the difference between airunner and afm-Server? airunner: Offline inference engine for art, real-time voice conversations, LLM powered chatbots and automated workflows. afm-Server: macOS menu bar app for exposing Apple's on-device Foundation Models via an OpenAI-compatible API. See the comparison table for live GitHub stats and shared categories. ### When should I choose airunner over afm-Server? Choose airunner over afm-Server when airunner is primarily Python; afm-Server is Swift; License: airunner is GPL-3.0, afm-Server is MIT; Requirements: Min 16 GB RAM; Requires Docker; Requires specific GPU support (NVIDIA) and larger storage allocations compared to competitors; Tags unique to airunner: ai-art, chatbot, image-generation, speech-to-text; Also covers Computer Vision, Speech & Audio; airunner ships Docker support for self-hosted deployment; When needing an all-inclusive offline tool for both image generation and speech-to-text/text-to-speech functionalities. ### When should I choose afm-Server over airunner? Choose afm-Server over airunner when afm-Server is primarily Swift; airunner is Python; License: afm-Server is MIT, airunner is GPL-3.0; Tags unique to afm-Server: apple-intelligence, foundation-models, local-llm, macos; When you need local, cloud-free inference services from Apple's device-based AI models and are working within a macOS environment. ### When should I avoid airunner? If your primary need is cloud-based services as AIRunner focuses on local deployments only In scenarios where minimal hardware requirements are crucial, given AIRunner's higher system demands (min. 16 GB RAM, NVIDIA RTX 3060) ### When should I avoid afm-Server? In scenarios where a cross-platform solution is necessary as afm-Server only supports macOS environments When your application demands real-time, high-throughput API access that can be limited by the device's hardware capabilities compared to cloud solutions ### Is airunner or afm-Server more popular on GitHub? airunner has more GitHub stars (1,312 vs 189). Stars measure visibility, not whether either tool fits your constraints. ### Are airunner and afm-Server open source? Yes - both are open-source projects on GitHub (airunner: GPL-3.0, afm-Server: MIT). ### Where can I find alternatives to airunner or afm-Server? GraphCanon lists graph-backed alternatives at [airunner alternatives](/tools/capsize-games-airunner/alternatives) and [afm-Server alternatives](/tools/techopolis-afm-server/alternatives) ([airunner markdown twin](/tools/capsize-games-airunner/alternatives.md), [afm-Server markdown twin](/tools/techopolis-afm-server/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/capsize-games-airunner-vs-techopolis-afm-server.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, airunner or afm-Server? airunner: Active. afm-Server: Steady. 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 airunner and afm-Server? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [airunner trust report](/tools/capsize-games-airunner/trust); [afm-Server trust report](/tools/techopolis-afm-server/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=capsize-games-airunner`](/api/graphcanon/graph?tool=capsize-games-airunner) - 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/_