--- title: "airllm vs LLMmap" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lyogavin-airllm-vs-pasquini-dario-llmmap" tools: ["lyogavin-airllm", "pasquini-dario-llmmap"] --- # airllm vs LLMmap *GraphCanon updated Jul 12, 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 LLMmap if lLMmap is a Python-based tool for quick inference using pretrained models without needing additional training. It includes PyTorch weights, configuration files, and behavioral templates tailored to 52 different LLMs. [airllm](https://github.com/lyogavin/airllm) reports 22k GitHub stars, 2.6k forks, and 106 open issues, last pushed Jul 11, 2026. [LLMmap](https://github.com/pasquini-dario/LLMmap) has 371 stars, 42 forks, and 6 open issues, last pushed Jul 24, 2025. Figures are from public GitHub metadata via [airllm's repository](https://github.com/lyogavin/airllm) and [LLMmap's repository](https://github.com/pasquini-dario/LLMmap). | | [airllm](/tools/lyogavin-airllm.md) | [LLMmap](/tools/pasquini-dario-llmmap.md) | | --- | --- | --- | | Tagline | AirLLM 70B inference with single 4GB GPU | Provides a ready-to-use pretrained model for open-set inference with PyTorch weights, configuration file, and behavioral templates. | | Stars | 22,399 | 371 | | Forks | 2,581 | 42 | | Open issues | 106 | 6 | | Language | Jupyter Notebook | Python | | Adopt for | AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU. | LLMmap is a Python-based tool for quick inference using pretrained models without needing additional training. It includes PyTorch weights, configuration files, and behavioral templates tailored to 52 different LLMs. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Inference & Serving | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [airllm](/tools/lyogavin-airllm.md) | [LLMmap](/tools/pasquini-dario-llmmap.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 352d | | Open issues (now) | 106 | 6 | | Security scan | 4 low (4 low) | 32 low (32 low) | | Full report | [trust report](/tools/lyogavin-airllm/trust.md) | [trust report](/tools/pasquini-dario-llmmap/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: LLMmap - **Adopt for:** LLMmap is a Python-based tool for quick inference using pretrained models without needing additional training. It includes PyTorch weights, configuration files, and behavioral templates tailored to 52 different LLMs. ## Choose when ### Choose airllm if… - airllm is primarily Jupyter Notebook; LLMmap is Python. - License: airllm is Apache-2.0, LLMmap is MIT. - 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 LLMmap if… - LLMmap is primarily Python; airllm is Jupyter Notebook. - License: LLMmap is MIT, airllm is Apache-2.0. - Tags unique to LLMmap: llms, open-set inference, pretrained models, python. - Also covers Model Training. - When you need immediate model deployment and don't want or can’t afford the time to train a custom model. ## 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 LLMmap - If your application requires fine-tuning on specific datasets as LLMmap offers only generic pretrained models without out-of-the-box support for further training. - In scenarios needing advanced customization beyond the provided behavioral templates, since LLMmap’s framework might not accommodate extensive model modifications. ## Common questions ### What is the difference between airllm and LLMmap? airllm: AirLLM 70B inference with single 4GB GPU. LLMmap: Provides a ready-to-use pretrained model for open-set inference with PyTorch weights, configuration file, and behavioral templates.. See the comparison table for live GitHub stats and shared categories. ### When should I choose airllm over LLMmap? Choose airllm over LLMmap when airllm is primarily Jupyter Notebook; LLMmap is Python; License: airllm is Apache-2.0, LLMmap is MIT; 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 LLMmap over airllm? Choose LLMmap over airllm when LLMmap is primarily Python; airllm is Jupyter Notebook; License: LLMmap is MIT, airllm is Apache-2.0; Tags unique to LLMmap: llms, open-set inference, pretrained models, python; Also covers Model Training; When you need immediate model deployment and don't want or can’t afford the time to train a custom model. ### 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 LLMmap? If your application requires fine-tuning on specific datasets as LLMmap offers only generic pretrained models without out-of-the-box support for further training. In scenarios needing advanced customization beyond the provided behavioral templates, since LLMmap’s framework might not accommodate extensive model modifications. ### Is airllm or LLMmap more popular on GitHub? airllm has more GitHub stars (22,399 vs 371). Stars measure visibility, not whether either tool fits your constraints. ### Are airllm and LLMmap open source? Yes - both are open-source projects on GitHub (airllm: Apache-2.0, LLMmap: MIT). ### Where can I find alternatives to airllm or LLMmap? GraphCanon lists graph-backed alternatives at [airllm alternatives](/tools/lyogavin-airllm/alternatives) and [LLMmap alternatives](/tools/pasquini-dario-llmmap/alternatives) ([airllm markdown twin](/tools/lyogavin-airllm/alternatives.md), [LLMmap markdown twin](/tools/pasquini-dario-llmmap/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-pasquini-dario-llmmap.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, airllm or LLMmap? airllm: Very active. LLMmap: Slowing. 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 LLMmap? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [airllm trust report](/tools/lyogavin-airllm/trust); [LLMmap trust report](/tools/pasquini-dario-llmmap/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/_