--- title: "airllm vs private-gpt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lyogavin-airllm-vs-zylon-ai-private-gpt" tools: ["lyogavin-airllm", "zylon-ai-private-gpt"] --- # airllm vs private-gpt *GraphCanon updated Jul 11, 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 private-gpt if privateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities,. [airllm](https://github.com/lyogavin/airllm) reports 22k GitHub stars, 2.6k forks, and 106 open issues, last pushed Jul 11, 2026. [private-gpt](https://www.zylon.ai/private-gpt) has 57k stars, 7.6k forks, and 5 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [airllm's repository](https://github.com/lyogavin/airllm) and [private-gpt's repository](https://github.com/zylon-ai/private-gpt). | | [airllm](/tools/lyogavin-airllm.md) | [private-gpt](/tools/zylon-ai-private-gpt.md) | | --- | --- | --- | | Tagline | AirLLM 70B inference with single 4GB GPU | Complete API layer for private AI applications on local models | | Stars | 22,399 | 57,329 | | Forks | 2,581 | 7,598 | | Open issues | 106 | 5 | | 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. | PrivateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities, | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | 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) | [private-gpt](/tools/zylon-ai-private-gpt.md) | | --- | --- | --- | | Open issues (now) | 106 | 5 | | Owner type | User | Organization | | Security scan | 4 low (4 low) | No lockfile | | Full report | [trust report](/tools/lyogavin-airllm/trust.md) | [trust report](/tools/zylon-ai-private-gpt/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: private-gpt - **Requirements:** Min 8 GB RAM; Requires Docker - **Adopt for:** PrivateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities, ## Choose when ### Choose airllm if… - airllm is primarily Jupyter Notebook; private-gpt is Python. - 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: llama, chinese llm, llm, instruct-gpt. - 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 private-gpt if… - private-gpt is primarily Python; airllm is Jupyter Notebook. - Requirements: Min 8 GB RAM; Requires Docker. - Tags unique to private-gpt: text-to-sql, ai, on-premise, tools. - private-gpt ships Docker support for self-hosted deployment. - - You need to deploy and operationalize your own locally-run models without relying on cloud APIs. ## 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 private-gpt - - You prefer simplicity and ease-of-use over full control; PrivateGPT requires more setup than using direct cloud-based AI services. - - Your project does not involve running models locally but strictly relies on public cloud resources for inference server operations. - - You do not have the technical capability to run an OpenAI-compatible inference server or manage local infrastructure effectively. ## Common questions ### What is the difference between airllm and private-gpt? airllm: AirLLM 70B inference with single 4GB GPU. private-gpt: Complete API layer for private AI applications on local models. See the comparison table for live GitHub stats and shared categories. ### When should I choose airllm over private-gpt? Choose airllm over private-gpt when airllm is primarily Jupyter Notebook; private-gpt is Python; 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: llama, chinese llm, llm, instruct-gpt; 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 private-gpt over airllm? Choose private-gpt over airllm when private-gpt is primarily Python; airllm is Jupyter Notebook; Requirements: Min 8 GB RAM; Requires Docker; Tags unique to private-gpt: text-to-sql, ai, on-premise, tools; private-gpt ships Docker support for self-hosted deployment; - You need to deploy and operationalize your own locally-run models without relying on cloud APIs. ### 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 private-gpt? - You prefer simplicity and ease-of-use over full control; PrivateGPT requires more setup than using direct cloud-based AI services. - Your project does not involve running models locally but strictly relies on public cloud resources for inference server operations. - You do not have the technical capability to run an OpenAI-compatible inference server or manage local infrastructure effectively. ### Is airllm or private-gpt more popular on GitHub? private-gpt has more GitHub stars (57,329 vs 22,399). Stars measure visibility, not whether either tool fits your constraints. ### Are airllm and private-gpt open source? Yes - both are open-source projects on GitHub (airllm: Apache-2.0, private-gpt: Apache-2.0). ### Where can I find alternatives to airllm or private-gpt? GraphCanon lists graph-backed alternatives at [airllm alternatives](/tools/lyogavin-airllm/alternatives) and [private-gpt alternatives](/tools/zylon-ai-private-gpt/alternatives) ([airllm markdown twin](/tools/lyogavin-airllm/alternatives.md), [private-gpt markdown twin](/tools/zylon-ai-private-gpt/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-zylon-ai-private-gpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, airllm or private-gpt? airllm: Very active. private-gpt: Very 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 airllm and private-gpt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [airllm trust report](/tools/lyogavin-airllm/trust); [private-gpt trust report](/tools/zylon-ai-private-gpt/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/_