--- title: "omlx vs anubis-oss" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jundot-omlx-vs-uncsoft-anubis-oss" tools: ["jundot-omlx", "uncsoft-anubis-oss"] --- # omlx vs anubis-oss *GraphCanon updated Sep 20, 2026* ## Verdict Pick omlx if omlx is an LLM inference server tailored for Apple Silicon that emphasizes continuous batching and SSD caching capabilities, accessible through macOS menu bar control or Homebrew installation; pick anubis-oss if anubis-oss, specifically tailored for Apple Silicon devices using Swift, is distinguished by its focus on local large language model evaluation and testing within the macOS environment. [omlx](https://omlx.ai) reports 22k GitHub stars, 1.9k forks, and 1.4k open issues, last pushed Sep 20, 2026. [anubis-oss](https://devpadapp.com/leaderboard.html) has 207 stars, 15 forks, and 1 open issues, last pushed Sep 5, 2026. Figures are from public GitHub metadata via [omlx's repository](https://github.com/jundot/omlx) and [anubis-oss's repository](https://github.com/uncSoft/anubis-oss). | | [omlx](/tools/jundot-omlx.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Tagline | LLM inference server with continuous batching and SSD caching for Apple Silicon | Local LLM Testing & Benchmarking for Apple Silicon | | Stars | 21,934 | 207 | | Forks | 1,899 | 15 | | Open issues | 1,407 | 1 | | Language | Python | Swift | | Adopt for | omlx is an LLM inference server tailored for Apple Silicon that emphasizes continuous batching and SSD caching capabilities, accessible through macOS menu bar control or Homebrew installation. | Anubis-oss, specifically tailored for Apple Silicon devices using Swift, is distinguished by its focus on local large language model evaluation and testing within the macOS environment. | | Persona | - | - | | Runtime | - | - | | License | omlx is available under the Apache License, Version 2.0 (Apache-2.0), a permissive free software license. | GPL-3.0 license ensures that any derivative works related to anubis-oss must also be open source under the same licensing terms. | | Categories | Inference & Serving | Evaluation & Observability, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [omlx](/tools/jundot-omlx.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 15d | | Open issues (now) | 1.4k | 1 | | Stars delta | +3.3k (30d) | +9 (30d) | | Open issues delta | +552 (30d) | -3 (30d) | | Full report | [trust report](/tools/jundot-omlx/trust.md) | [trust report](/tools/uncsoft-anubis-oss/trust.md) | ## Decision facts: omlx - **Adopt for:** omlx is an LLM inference server tailored for Apple Silicon that emphasizes continuous batching and SSD caching capabilities, accessible through macOS menu bar control or Homebrew installation. - **License detail:** omlx is available under the Apache License, Version 2.0 (Apache-2.0), a permissive free software license. ## Decision facts: anubis-oss - **Pricing:** freemium - The tool is free and open-source with no monetary costs for usage or distribution. - **Requirements:** Min 8 GB RAM - **Adopt for:** Anubis-oss, specifically tailored for Apple Silicon devices using Swift, is distinguished by its focus on local large language model evaluation and testing within the macOS environment. - **License detail:** GPL-3.0 license ensures that any derivative works related to anubis-oss must also be open source under the same licensing terms. ## Choose when ### Choose omlx if… - omlx is primarily Python; anubis-oss is Swift. - License: omlx is Apache-2.0, anubis-oss is GPL-3.0. - Tags unique to omlx: inference-server. - If your primary computing environment is based on Apple Silicon devices, omlx offers optimized performance for running large language model inferences. ### Choose anubis-oss if… - anubis-oss is primarily Swift; omlx is Python. - License: anubis-oss is GPL-3.0, omlx is Apache-2.0. - Pricing: The tool is free and open-source with no monetary costs for usage or distribution.. - Requirements: Min 8 GB RAM. - Tags unique to anubis-oss: benchmarking, gpu, inference, local-llm. - Also covers Evaluation & Observability. - When developing and evaluating large language models intended to run natively on Apple Silicon hardware. ## When NOT to use omlx - If your development infrastructure relies on non-Apple Silicon hardware, omlx's specific optimizations will not be as beneficial. - Teams that require cross-platform compatibility or run servers predominantly on non-macOS operating systems should consider alternatives with broader support. - For environments where direct control through the menu bar is not practical or desired. ## When NOT to use anubis-oss - If your development does not involve Apple Silicon or macOS environments as Anubis-oss is tightly integrated with these platforms. - When preferring a language other than Swift, since Anubis-oss depends on this for its operations. ## Common questions ### What is the difference between omlx and anubis-oss? omlx: LLM inference server with continuous batching and SSD caching for Apple Silicon. anubis-oss: Local LLM Testing & Benchmarking for Apple Silicon. See the comparison table for live GitHub stats and shared categories. ### When should I choose omlx over anubis-oss? Choose omlx over anubis-oss when omlx is primarily Python; anubis-oss is Swift; License: omlx is Apache-2.0, anubis-oss is GPL-3.0; Tags unique to omlx: inference-server; If your primary computing environment is based on Apple Silicon devices, omlx offers optimized performance for running large language model inferences. ### When should I choose anubis-oss over omlx? Choose anubis-oss over omlx when anubis-oss is primarily Swift; omlx is Python; License: anubis-oss is GPL-3.0, omlx is Apache-2.0; Pricing: The tool is free and open-source with no monetary costs for usage or distribution.; Requirements: Min 8 GB RAM; Tags unique to anubis-oss: benchmarking, gpu, inference, local-llm; Also covers Evaluation & Observability; When developing and evaluating large language models intended to run natively on Apple Silicon hardware. ### When should I avoid omlx? If your development infrastructure relies on non-Apple Silicon hardware, omlx's specific optimizations will not be as beneficial. Teams that require cross-platform compatibility or run servers predominantly on non-macOS operating systems should consider alternatives with broader support. For environments where direct control through the menu bar is not practical or desired. ### When should I avoid anubis-oss? If your development does not involve Apple Silicon or macOS environments as Anubis-oss is tightly integrated with these platforms. When preferring a language other than Swift, since Anubis-oss depends on this for its operations. ### Is omlx or anubis-oss more popular on GitHub? omlx has more GitHub stars (21,934 vs 207). Stars measure visibility, not whether either tool fits your constraints. ### Are omlx and anubis-oss open source? Yes - both are open-source projects on GitHub (omlx: Apache-2.0, anubis-oss: GPL-3.0). ### Where can I find alternatives to omlx or anubis-oss? GraphCanon lists graph-backed alternatives at [omlx alternatives](/tools/jundot-omlx/alternatives) and [anubis-oss alternatives](/tools/uncsoft-anubis-oss/alternatives) ([omlx markdown twin](/tools/jundot-omlx/alternatives.md), [anubis-oss markdown twin](/tools/uncsoft-anubis-oss/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/jundot-omlx-vs-uncsoft-anubis-oss.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, omlx or anubis-oss? omlx: Very active. anubis-oss: 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 omlx and anubis-oss? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [omlx trust report](/tools/jundot-omlx/trust); [anubis-oss trust report](/tools/uncsoft-anubis-oss/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jundot-omlx`](/api/graphcanon/graph?tool=jundot-omlx) - 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/_