--- title: "airllm vs deploy-llms-with-ansible" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lyogavin-airllm-vs-xamey-deploy-llms-with-ansible" tools: ["lyogavin-airllm", "xamey-deploy-llms-with-ansible"] --- # airllm vs deploy-llms-with-ansible *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 deploy-llms-with-ansible if deploy-llms-with-ansible. [airllm](https://github.com/lyogavin/airllm) reports 22k GitHub stars, 2.6k forks, and 106 open issues, last pushed Jul 11, 2026. [deploy-llms-with-ansible](https://github.com/xamey/deploy-llms-with-ansible) has 3 stars, 0 forks, and 0 open issues, last pushed May 1, 2025. Figures are from public GitHub metadata via [airllm's repository](https://github.com/lyogavin/airllm) and [deploy-llms-with-ansible's repository](https://github.com/xamey/deploy-llms-with-ansible). | | [airllm](/tools/lyogavin-airllm.md) | [deploy-llms-with-ansible](/tools/xamey-deploy-llms-with-ansible.md) | | --- | --- | --- | | Tagline | AirLLM 70B inference with single 4GB GPU | Easily deploy LLMs using Ansible | | Stars | 22,399 | 3 | | Forks | 2,581 | 0 | | Open issues | 106 | 0 | | Language | Jupyter Notebook | - | | Adopt for | AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU. | deploy-llms-with-ansible | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [airllm](/tools/lyogavin-airllm.md) | [deploy-llms-with-ansible](/tools/xamey-deploy-llms-with-ansible.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 435d | | Open issues (now) | 106 | 0 | | Security scan | 4 low (4 low) | No lockfile | | Full report | [trust report](/tools/lyogavin-airllm/trust.md) | [trust report](/tools/xamey-deploy-llms-with-ansible/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: deploy-llms-with-ansible - **Pricing:** unknown - **Requirements:** Requires Docker; Requires Ansible installed and configured on the local machine.; Debian-based VM with SSH access and Docker must be present. - **Adopt for:** deploy-llms-with-ansible ## Choose when ### Choose airllm if… - 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 deploy-llms-with-ansible if… - Requirements: Requires Docker; Requires Ansible installed and configured on the local machine.; Debian-based VM with SSH access and Docker must be present.. - Tags unique to deploy-llms-with-ansible: ansible, deployment, docker, llama-cpp. - When you prefer using Ansible to automate the deployment of LLMs on a Debian-based virtual machine equipped with Docker. ## 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 deploy-llms-with-ansible - When working in an environment that uses alternative automation tools like Terraform or Chef, as this tool specifically requires Ansible knowledge. - If the infrastructure does not support or permit the use of Docker for containerizing applications. - In cases where extensive customization of models beyond what llama.cpp and Ollama offer is required. ## Common questions ### What is the difference between airllm and deploy-llms-with-ansible? airllm: AirLLM 70B inference with single 4GB GPU. deploy-llms-with-ansible: Easily deploy LLMs using Ansible. See the comparison table for live GitHub stats and shared categories. ### When should I choose airllm over deploy-llms-with-ansible? Choose airllm over deploy-llms-with-ansible when 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 deploy-llms-with-ansible over airllm? Choose deploy-llms-with-ansible over airllm when Requirements: Requires Docker; Requires Ansible installed and configured on the local machine.; Debian-based VM with SSH access and Docker must be present.; Tags unique to deploy-llms-with-ansible: ansible, deployment, docker, llama-cpp; When you prefer using Ansible to automate the deployment of LLMs on a Debian-based virtual machine equipped with Docker. ### 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 deploy-llms-with-ansible? When working in an environment that uses alternative automation tools like Terraform or Chef, as this tool specifically requires Ansible knowledge. If the infrastructure does not support or permit the use of Docker for containerizing applications. In cases where extensive customization of models beyond what llama.cpp and Ollama offer is required. ### Is airllm or deploy-llms-with-ansible more popular on GitHub? airllm has more GitHub stars (22,399 vs 3). Stars measure visibility, not whether either tool fits your constraints. ### Are airllm and deploy-llms-with-ansible open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to airllm or deploy-llms-with-ansible? GraphCanon lists graph-backed alternatives at [airllm alternatives](/tools/lyogavin-airllm/alternatives) and [deploy-llms-with-ansible alternatives](/tools/xamey-deploy-llms-with-ansible/alternatives) ([airllm markdown twin](/tools/lyogavin-airllm/alternatives.md), [deploy-llms-with-ansible markdown twin](/tools/xamey-deploy-llms-with-ansible/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-xamey-deploy-llms-with-ansible.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, airllm or deploy-llms-with-ansible? airllm: Very active. deploy-llms-with-ansible: 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 deploy-llms-with-ansible? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [airllm trust report](/tools/lyogavin-airllm/trust); [deploy-llms-with-ansible trust report](/tools/xamey-deploy-llms-with-ansible/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/_