--- title: "airllm vs anubis-oss" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lyogavin-airllm-vs-uncsoft-anubis-oss" tools: ["lyogavin-airllm", "uncsoft-anubis-oss"] --- # airllm vs anubis-oss *GraphCanon updated Aug 13, 2026* ## Verdict Pick airllm if airLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU; 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. [airllm](https://github.com/lyogavin/airllm) reports 24k GitHub stars, 2.7k forks, and 115 open issues, last pushed Jul 23, 2026. [anubis-oss](https://devpadapp.com/leaderboard.html) has 198 stars, 12 forks, and 4 open issues, last pushed Jun 18, 2026. Figures are from public GitHub metadata via [airllm's repository](https://github.com/lyogavin/airllm) and [anubis-oss's repository](https://github.com/uncSoft/anubis-oss). | | [airllm](/tools/lyogavin-airllm.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Tagline | AirLLM 70B inference with single 4GB GPU | Local LLM Testing & Benchmarking for Apple Silicon | | Stars | 24,183 | 198 | | Forks | 2,722 | 12 | | Open issues | 115 | 4 | | Language | Jupyter Notebook | Swift | | Adopt for | AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU. | 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 | Apache-2.0 | 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._ | | [airllm](/tools/lyogavin-airllm.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 5d | 56d | | Open issues (now) | 115 | 4 | | Full report | [trust report](/tools/lyogavin-airllm/trust.md) | [trust report](/tools/uncsoft-anubis-oss/trust.md) | ## Decision facts: airllm - **Pricing:** freemium - Free and open-source under the Apache-2.0 license; however, infrastructure costs apply. - **Requirements:** Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences. - **Adopt for:** AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU. - **License detail:** Apache-2.0 ## 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 airllm if… - airllm is primarily Jupyter Notebook; anubis-oss is Swift. - License: airllm is Apache-2.0, anubis-oss is GPL-3.0. - Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply.. - Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences.. - Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai. - If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM. ### Choose anubis-oss if… - anubis-oss is primarily Swift; airllm is Jupyter Notebook. - License: anubis-oss is GPL-3.0, airllm 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: apple-silicon, benchmarking, gpu, inference. - Also covers Evaluation & Observability. - When developing and evaluating large language models intended to run natively on Apple Silicon hardware. ## When NOT to use airllm - Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency. - Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition. ## 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 airllm and anubis-oss? airllm: AirLLM 70B inference with single 4GB GPU. anubis-oss: Local LLM Testing & Benchmarking for Apple Silicon. See the comparison table for live GitHub stats and shared categories. ### When should I choose airllm over anubis-oss? Choose airllm over anubis-oss when airllm is primarily Jupyter Notebook; anubis-oss is Swift; License: airllm is Apache-2.0, anubis-oss is GPL-3.0; Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply.; Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences.; Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai; If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM. ### When should I choose anubis-oss over airllm? Choose anubis-oss over airllm when anubis-oss is primarily Swift; airllm is Jupyter Notebook; License: anubis-oss is GPL-3.0, airllm 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: apple-silicon, benchmarking, gpu, inference; Also covers Evaluation & Observability; When developing and evaluating large language models intended to run natively on Apple Silicon hardware. ### When should I avoid airllm? Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency. Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition. ### 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 airllm or anubis-oss more popular on GitHub? airllm has more GitHub stars (24,183 vs 198). Stars measure visibility, not whether either tool fits your constraints. ### Are airllm and anubis-oss open source? Yes - both are open-source projects on GitHub (airllm: Apache-2.0, anubis-oss: GPL-3.0). ### Where can I find alternatives to airllm or anubis-oss? GraphCanon lists graph-backed alternatives at [airllm alternatives](/tools/lyogavin-airllm/alternatives) and [anubis-oss alternatives](/tools/uncsoft-anubis-oss/alternatives) ([airllm markdown twin](/tools/lyogavin-airllm/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/lyogavin-airllm-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, airllm or anubis-oss? airllm: Very active. anubis-oss: 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 airllm and anubis-oss? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [airllm trust report](/tools/lyogavin-airllm/trust); [anubis-oss trust report](/tools/uncsoft-anubis-oss/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lyogavin-airllm`](/api/graphcanon/graph?tool=lyogavin-airllm) - 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/_