--- title: "budgetml vs airllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ebhy-budgetml-vs-lyogavin-airllm" tools: ["ebhy-budgetml", "lyogavin-airllm"] --- # budgetml vs airllm *GraphCanon updated Jul 11, 2026* ## Verdict Pick budgetml when budgetml is primarily Python; airllm is Jupyter Notebook; pick airllm when airllm is primarily Jupyter Notebook; budgetml is Python. [budgetml](https://github.com/ebhy/budgetml) reports 1.3k GitHub stars, 65 forks, and 4 open issues, last pushed Feb 12, 2024. [airllm](https://github.com/lyogavin/airllm) has 22k stars, 2.6k forks, and 106 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [budgetml's repository](https://github.com/ebhy/budgetml) and [airllm's repository](https://github.com/lyogavin/airllm). | | [budgetml](/tools/ebhy-budgetml.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Tagline | Deploy a ML inference service on a budget in less than 10 lines of code. | AirLLM 70B inference with single 4GB GPU | | Stars | 1,343 | 22,399 | | Forks | 65 | 2,581 | | Open issues | 4 | 106 | | Language | Python | 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. | | 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._ | | [budgetml](/tools/ebhy-budgetml.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 880d | 0d | | Open issues (now) | 4 | 106 | | Owner type | Organization | User | | Security scan | No criticals | 4 low (4 low) | | Full report | [trust report](/tools/ebhy-budgetml/trust.md) | [trust report](/tools/lyogavin-airllm/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 ## Choose when ### Choose budgetml if… - budgetml is primarily Python; airllm is Jupyter Notebook. - Tags unique to budgetml: data-science, deployment, machine-learning, python. - Leaner open-issue backlog (4). ### Choose airllm if… - airllm is primarily Jupyter Notebook; budgetml 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 NOT to use budgetml - Last GitHub push was 880 days ago (dormant maintenance, Feb 12, 2024). Validate activity before betting a new project on budgetml. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## 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. ## Common questions ### What is the difference between budgetml and airllm? budgetml: Deploy a ML inference service on a budget in less than 10 lines of code.. airllm: AirLLM 70B inference with single 4GB GPU. See the comparison table for live GitHub stats and shared categories. ### When should I choose budgetml over airllm? Choose budgetml over airllm when budgetml is primarily Python; airllm is Jupyter Notebook; Tags unique to budgetml: data-science, deployment, machine-learning, python; Leaner open-issue backlog (4). ### When should I choose airllm over budgetml? Choose airllm over budgetml when airllm is primarily Jupyter Notebook; budgetml 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 avoid budgetml? Last GitHub push was 880 days ago (dormant maintenance, Feb 12, 2024). Validate activity before betting a new project on budgetml. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### 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. ### Is budgetml or airllm more popular on GitHub? airllm has more GitHub stars (22,399 vs 1,343). Stars measure visibility, not whether either tool fits your constraints. ### Are budgetml and airllm open source? Yes - both are open-source projects on GitHub (budgetml: Apache-2.0, airllm: Apache-2.0). ### Where can I find alternatives to budgetml or airllm? GraphCanon lists graph-backed alternatives at [budgetml alternatives](/tools/ebhy-budgetml/alternatives) and [airllm alternatives](/tools/lyogavin-airllm/alternatives) ([budgetml markdown twin](/tools/ebhy-budgetml/alternatives.md), [airllm markdown twin](/tools/lyogavin-airllm/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/ebhy-budgetml-vs-lyogavin-airllm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, budgetml or airllm? budgetml: Dormant. airllm: 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 budgetml and airllm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [budgetml trust report](/tools/ebhy-budgetml/trust); [airllm trust report](/tools/lyogavin-airllm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ebhy-budgetml`](/api/graphcanon/graph?tool=ebhy-budgetml) - 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/_