--- title: "tiny-vllm vs anubis-oss" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jmaczan-tiny-vllm-vs-uncsoft-anubis-oss" tools: ["jmaczan-tiny-vllm", "uncsoft-anubis-oss"] --- # tiny-vllm vs anubis-oss *GraphCanon updated Aug 25, 2026* ## Verdict Pick tiny-vllm if for those needing a compact yet potent LLM inference engine built on C++ and CUDA, tiny-vllm presents an accessible framework inspired by its larger sibling, vLLM; 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. [tiny-vllm](https://github.com/jmaczan/tiny-vllm) reports 1.1k GitHub stars, 84 forks, and 0 open issues, last pushed Aug 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 [tiny-vllm's repository](https://github.com/jmaczan/tiny-vllm) and [anubis-oss's repository](https://github.com/uncSoft/anubis-oss). | | [tiny-vllm](/tools/jmaczan-tiny-vllm.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Tagline | Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM | Local LLM Testing & Benchmarking for Apple Silicon | | Stars | 1,075 | 198 | | Forks | 84 | 12 | | Open issues | 0 | 4 | | Language | C++ | Swift | | Adopt for | For those needing a compact yet potent LLM inference engine built on C++ and CUDA, tiny-vllm presents an accessible framework inspired by its larger sibling, vLLM. | 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._ | | [tiny-vllm](/tools/jmaczan-tiny-vllm.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 1d | 56d | | Open issues (now) | 0 | 4 | | Stars delta | +128 (30d) | Unknown | | Open issues delta | -2 (30d) | Unknown | | Full report | [trust report](/tools/jmaczan-tiny-vllm/trust.md) | [trust report](/tools/uncsoft-anubis-oss/trust.md) | ## Decision facts: tiny-vllm - **Adopt for:** For those needing a compact yet potent LLM inference engine built on C++ and CUDA, tiny-vllm presents an accessible framework inspired by its larger sibling, vLLM. ## 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 tiny-vllm if… - tiny-vllm is primarily C++; anubis-oss is Swift. - License: tiny-vllm is Apache-2.0, anubis-oss is GPL-3.0. - Tags unique to tiny-vllm: cuda, hpc, lstm. - When you require a lightweight solution for deploying large language model inference in environments with limited resources but still demand high performance. ### Choose anubis-oss if… - anubis-oss is primarily Swift; tiny-vllm is C++. - License: anubis-oss is GPL-3.0, tiny-vllm 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 tiny-vllm - Avoid using tiny-vllm if the application requires the full feature set offered by its larger counterpart, vLLM, as it has been trimmed for lightweight use. - Do not choose this tool when working in environments that do not support CUDA or where a higher abstraction level is preferred over direct C++ and CUDA implementation. ## 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 tiny-vllm and anubis-oss? tiny-vllm: Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM. anubis-oss: Local LLM Testing & Benchmarking for Apple Silicon. See the comparison table for live GitHub stats and shared categories. ### When should I choose tiny-vllm over anubis-oss? Choose tiny-vllm over anubis-oss when tiny-vllm is primarily C++; anubis-oss is Swift; License: tiny-vllm is Apache-2.0, anubis-oss is GPL-3.0; Tags unique to tiny-vllm: cuda, hpc, lstm; When you require a lightweight solution for deploying large language model inference in environments with limited resources but still demand high performance. ### When should I choose anubis-oss over tiny-vllm? Choose anubis-oss over tiny-vllm when anubis-oss is primarily Swift; tiny-vllm is C++; License: anubis-oss is GPL-3.0, tiny-vllm 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 tiny-vllm? Avoid using tiny-vllm if the application requires the full feature set offered by its larger counterpart, vLLM, as it has been trimmed for lightweight use. Do not choose this tool when working in environments that do not support CUDA or where a higher abstraction level is preferred over direct C++ and CUDA implementation. ### 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 tiny-vllm or anubis-oss more popular on GitHub? tiny-vllm has more GitHub stars (1,075 vs 198). Stars measure visibility, not whether either tool fits your constraints. ### Are tiny-vllm and anubis-oss open source? Yes - both are open-source projects on GitHub (tiny-vllm: Apache-2.0, anubis-oss: GPL-3.0). ### Where can I find alternatives to tiny-vllm or anubis-oss? GraphCanon lists graph-backed alternatives at [tiny-vllm alternatives](/tools/jmaczan-tiny-vllm/alternatives) and [anubis-oss alternatives](/tools/uncsoft-anubis-oss/alternatives) ([tiny-vllm markdown twin](/tools/jmaczan-tiny-vllm/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/jmaczan-tiny-vllm-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, tiny-vllm or anubis-oss? tiny-vllm: 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 tiny-vllm and anubis-oss? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [tiny-vllm trust report](/tools/jmaczan-tiny-vllm/trust); [anubis-oss trust report](/tools/uncsoft-anubis-oss/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jmaczan-tiny-vllm`](/api/graphcanon/graph?tool=jmaczan-tiny-vllm) - 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/_