--- title: "airllm vs prompt-patterns" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lyogavin-airllm-vs-phodal-prompt-patterns" tools: ["lyogavin-airllm", "phodal-prompt-patterns"] --- # airllm vs prompt-patterns *GraphCanon updated Sep 20, 2026* ## Verdict Pick airllm when requirements: Min 4 GB RAM; Installation is straightforward via pip.; pick prompt-patterns when requirements: The repository does not specify system requirements.; Details about compatible AI models, frameworks, and the expected environment setup remain unspecified in the provided information.. [airllm](https://github.com/lyogavin/airllm) reports 35k GitHub stars, 3.6k forks, and 155 open issues, last pushed Sep 18, 2026. [prompt-patterns](https://prompt-patterns.phodal.com) has 3.1k stars, 198 forks, and 0 open issues, last pushed Mar 22, 2023. Figures are from public GitHub metadata via [airllm's repository](https://github.com/lyogavin/airllm) and [prompt-patterns's repository](https://github.com/phodal/prompt-patterns). | | [airllm](/tools/lyogavin-airllm.md) | [prompt-patterns](/tools/phodal-prompt-patterns.md) | | --- | --- | --- | | Tagline | Inference for AirLLM 70B model with single 4GB GPU | Prompt 编写模式：如何将思维框架赋予机器，以设计模式的形式来思考 prompt | | Stars | 34,504 | 3,091 | | Forks | 3,626 | 198 | | Open issues | 155 | 0 | | Language | Jupyter Notebook | - | | Adopt for | AirLLM is a repository that enables the inference of a 70B parameter model using a single 4GB GPU, with a focus on Chinese language models and generative AI. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Inference & Serving, LLM Frameworks | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [airllm](/tools/lyogavin-airllm.md) | [prompt-patterns](/tools/phodal-prompt-patterns.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1278d | | Open issues (now) | 155 | 0 | | Stars delta | +10k (30d) | -4 (30d) | | Open issues delta | +40 (30d) | 0 (30d) | | Full report | [trust report](/tools/lyogavin-airllm/trust.md) | [trust report](/tools/phodal-prompt-patterns/trust.md) | ## Decision facts: airllm - **Requirements:** Min 4 GB RAM; Installation is straightforward via pip. - **Adopt for:** AirLLM is a repository that enables the inference of a 70B parameter model using a single 4GB GPU, with a focus on Chinese language models and generative AI. - **License detail:** Apache-2.0 ## Decision facts: prompt-patterns - **Requirements:** The repository does not specify system requirements.; Details about compatible AI models, frameworks, and the expected environment setup remain unspecified in the provided information. ## Choose when ### Choose airllm if… - Requirements: Min 4 GB RAM; Installation is straightforward via pip.. - Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai. - Also covers Inference & Serving. - When you need to perform inference on a 70B parameter model with limited GPU resources, such as a single 4GB GPU. ### Choose prompt-patterns if… - Requirements: The repository does not specify system requirements.; Details about compatible AI models, frameworks, and the expected environment setup remain unspecified in the provided information.. - Tags unique to prompt-patterns: chatgpt, github-copilot, prompt-engineering, stable-diffusion. - Also covers Evaluation & Observability. - Use prompt-patterns for designing structured prompts to guide AI thinking in specific frameworks when working on projects that require maintaining a clear cognitive structure. ## When NOT to use airllm - If your project requires inference on models larger than 70B parameters or if you have access to more powerful GPUs than a 4GB model. - For projects that do not involve Chinese language models or generative AI tasks, as AirLLM is specifically optimized for these areas. ## When NOT to use prompt-patterns - Avoid prompt-patterns for real-time applications or situations requiring dynamic, contextually adaptive prompts, as it might lack flexibility compared to more generalized frameworks. - Do not use if your project's requirements revolve around innovative and unstructured AI interactions; this tool is best for structured environments. ## Common questions ### What is the difference between airllm and prompt-patterns? airllm: Inference for AirLLM 70B model with single 4GB GPU. prompt-patterns: Prompt 编写模式：如何将思维框架赋予机器，以设计模式的形式来思考 prompt. See the comparison table for live GitHub stats and shared categories. ### When should I choose airllm over prompt-patterns? Choose airllm over prompt-patterns when Requirements: Min 4 GB RAM; Installation is straightforward via pip.; Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai; Also covers Inference & Serving; When you need to perform inference on a 70B parameter model with limited GPU resources, such as a single 4GB GPU. ### When should I choose prompt-patterns over airllm? Choose prompt-patterns over airllm when Requirements: The repository does not specify system requirements.; Details about compatible AI models, frameworks, and the expected environment setup remain unspecified in the provided information.; Tags unique to prompt-patterns: chatgpt, github-copilot, prompt-engineering, stable-diffusion; Also covers Evaluation & Observability; Use prompt-patterns for designing structured prompts to guide AI thinking in specific frameworks when working on projects that require maintaining a clear cognitive structure. ### When should I avoid airllm? If your project requires inference on models larger than 70B parameters or if you have access to more powerful GPUs than a 4GB model. For projects that do not involve Chinese language models or generative AI tasks, as AirLLM is specifically optimized for these areas. ### When should I avoid prompt-patterns? Avoid prompt-patterns for real-time applications or situations requiring dynamic, contextually adaptive prompts, as it might lack flexibility compared to more generalized frameworks. Do not use if your project's requirements revolve around innovative and unstructured AI interactions; this tool is best for structured environments. ### Is airllm or prompt-patterns more popular on GitHub? airllm has more GitHub stars (34,504 vs 3,091). Stars measure visibility, not whether either tool fits your constraints. ### Are airllm and prompt-patterns open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to airllm or prompt-patterns? GraphCanon lists graph-backed alternatives at [airllm alternatives](/tools/lyogavin-airllm/alternatives) and [prompt-patterns alternatives](/tools/phodal-prompt-patterns/alternatives) ([airllm markdown twin](/tools/lyogavin-airllm/alternatives.md), [prompt-patterns markdown twin](/tools/phodal-prompt-patterns/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-phodal-prompt-patterns.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, airllm or prompt-patterns? airllm: Very active. prompt-patterns: Dormant. 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 prompt-patterns? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [airllm trust report](/tools/lyogavin-airllm/trust); [prompt-patterns trust report](/tools/phodal-prompt-patterns/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/_