--- title: "pratical-llms vs anubis-oss" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/antoniogr7-pratical-llms-vs-uncsoft-anubis-oss" tools: ["antoniogr7-pratical-llms", "uncsoft-anubis-oss"] --- # pratical-llms vs anubis-oss *GraphCanon updated Sep 20, 2026* ## Verdict Pick pratical-llms if practical-llms is a collection of Jupyter Notebooks aimed at LLM practitioners with practical guidance on quantization, sharding, inference and evaluation techniques; 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. [pratical-llms](https://github.com/AntonioGr7/pratical-llms) reports 53 GitHub stars, 15 forks, and 0 open issues, last pushed Jan 13, 2025. [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 [pratical-llms's repository](https://github.com/AntonioGr7/pratical-llms) and [anubis-oss's repository](https://github.com/uncSoft/anubis-oss). | | [pratical-llms](/tools/antoniogr7-pratical-llms.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Tagline | A collection of hands-on notebooks for LLM practitioners | Local LLM Testing & Benchmarking for Apple Silicon | | Stars | 53 | 207 | | Forks | 15 | 15 | | Open issues | 0 | 1 | | Language | Jupyter Notebook | Swift | | Adopt for | practical-llms is a collection of Jupyter Notebooks aimed at LLM practitioners with practical guidance on quantization, sharding, inference and evaluation techniques. | 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 | - | GPL-3.0 license ensures that any derivative works related to anubis-oss must also be open source under the same licensing terms. | | Categories | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [pratical-llms](/tools/antoniogr7-pratical-llms.md) | [anubis-oss](/tools/uncsoft-anubis-oss.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 604d | 15d | | Open issues (now) | 0 | 1 | | Stars delta | 0 (30d) | +9 (30d) | | Open issues delta | 0 (30d) | -3 (30d) | | Full report | [trust report](/tools/antoniogr7-pratical-llms/trust.md) | [trust report](/tools/uncsoft-anubis-oss/trust.md) | ## Decision facts: pratical-llms - **Adopt for:** practical-llms is a collection of Jupyter Notebooks aimed at LLM practitioners with practical guidance on quantization, sharding, inference and evaluation techniques. ## 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 pratical-llms if… - pratical-llms is primarily Jupyter Notebook; anubis-oss is Swift. - Tags unique to pratical-llms: genai, llm-evaluation, llm-inference, llm-serving. - Also covers LLM Frameworks, Model Training. - If you want to explore specific quantization methods like BitandBytes, GPTQ, exllamav2, or Half-Quadratic Quantization (HQQ). ### Choose anubis-oss if… - anubis-oss is primarily Swift; pratical-llms is Jupyter Notebook. - 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. - When developing and evaluating large language models intended to run natively on Apple Silicon hardware. ## When NOT to use pratical-llms - If you seek deep theoretical insights rather than practical implementation details. - For users looking for commercial support as this repository does not provide it, unlike some competitors. ## 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 pratical-llms and anubis-oss? pratical-llms: A collection of hands-on notebooks for LLM practitioners. anubis-oss: Local LLM Testing & Benchmarking for Apple Silicon. See the comparison table for live GitHub stats and shared categories. ### When should I choose pratical-llms over anubis-oss? Choose pratical-llms over anubis-oss when pratical-llms is primarily Jupyter Notebook; anubis-oss is Swift; Tags unique to pratical-llms: genai, llm-evaluation, llm-inference, llm-serving; Also covers LLM Frameworks, Model Training; If you want to explore specific quantization methods like BitandBytes, GPTQ, exllamav2, or Half-Quadratic Quantization (HQQ). ### When should I choose anubis-oss over pratical-llms? Choose anubis-oss over pratical-llms when anubis-oss is primarily Swift; pratical-llms is Jupyter Notebook; 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; When developing and evaluating large language models intended to run natively on Apple Silicon hardware. ### When should I avoid pratical-llms? If you seek deep theoretical insights rather than practical implementation details. For users looking for commercial support as this repository does not provide it, unlike some competitors. ### 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 pratical-llms or anubis-oss more popular on GitHub? anubis-oss has more GitHub stars (207 vs 53). Stars measure visibility, not whether either tool fits your constraints. ### Are pratical-llms and anubis-oss open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to pratical-llms or anubis-oss? GraphCanon lists graph-backed alternatives at [pratical-llms alternatives](/tools/antoniogr7-pratical-llms/alternatives) and [anubis-oss alternatives](/tools/uncsoft-anubis-oss/alternatives) ([pratical-llms markdown twin](/tools/antoniogr7-pratical-llms/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/antoniogr7-pratical-llms-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, pratical-llms or anubis-oss? pratical-llms: Dormant. 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 pratical-llms and anubis-oss? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [pratical-llms trust report](/tools/antoniogr7-pratical-llms/trust); [anubis-oss trust report](/tools/uncsoft-anubis-oss/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=antoniogr7-pratical-llms`](/api/graphcanon/graph?tool=antoniogr7-pratical-llms) - 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/_