--- title: "airllm vs codellama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lyogavin-airllm-vs-meta-llama-codellama" tools: ["lyogavin-airllm", "meta-llama-codellama"] --- # airllm vs codellama *GraphCanon updated Jul 11, 2026* ## Verdict Pick airllm when airllm is primarily Jupyter Notebook; codellama is Python; pick codellama when codellama is primarily Python; airllm is Jupyter Notebook. [airllm](https://github.com/lyogavin/airllm) reports 22k GitHub stars, 2.6k forks, and 106 open issues, last pushed Jul 11, 2026. [codellama](https://github.com/meta-llama/codellama) has 16k stars, 1.9k forks, and 116 open issues, last pushed Aug 12, 2024. Figures are from public GitHub metadata via [airllm's repository](https://github.com/lyogavin/airllm) and [codellama's repository](https://github.com/meta-llama/codellama). | | [airllm](/tools/lyogavin-airllm.md) | [codellama](/tools/meta-llama-codellama.md) | | --- | --- | --- | | Tagline | AirLLM 70B inference with single 4GB GPU | Inference code for CodeLlama models | | Stars | 22,399 | 16,298 | | Forks | 2,581 | 1,941 | | Open issues | 106 | 116 | | 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. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [airllm](/tools/lyogavin-airllm.md) | [codellama](/tools/meta-llama-codellama.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Archived (8%) | | Days since push | 0d | 698d | | Archived on GitHub | No | Yes | | Open issues (now) | 106 | 116 | | Owner type | User | Organization | | Security scan | 4 low (4 low) | No criticals | | Full report | [trust report](/tools/lyogavin-airllm/trust.md) | [trust report](/tools/meta-llama-codellama/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 airllm if… - airllm is primarily Jupyter Notebook; codellama is Python. - License: airllm is Apache-2.0, codellama is Other. - 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 codellama if… - codellama is primarily Python; airllm is Jupyter Notebook. - License: codellama is Other, airllm is Apache-2.0. - Tags unique to codellama: python. ## 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 codellama - codellama is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## Common questions ### What is the difference between airllm and codellama? airllm: AirLLM 70B inference with single 4GB GPU. codellama: Inference code for CodeLlama models. See the comparison table for live GitHub stats and shared categories. ### When should I choose airllm over codellama? Choose airllm over codellama when airllm is primarily Jupyter Notebook; codellama is Python; License: airllm is Apache-2.0, codellama is Other; 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 codellama over airllm? Choose codellama over airllm when codellama is primarily Python; airllm is Jupyter Notebook; License: codellama is Other, airllm is Apache-2.0; Tags unique to codellama: python. ### 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 codellama? codellama is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is airllm or codellama more popular on GitHub? airllm has more GitHub stars (22,399 vs 16,298). Stars measure visibility, not whether either tool fits your constraints. ### Are airllm and codellama open source? Yes - both are open-source projects on GitHub (airllm: Apache-2.0, codellama: Other). ### Where can I find alternatives to airllm or codellama? GraphCanon lists graph-backed alternatives at [airllm alternatives](/tools/lyogavin-airllm/alternatives) and [codellama alternatives](/tools/meta-llama-codellama/alternatives) ([airllm markdown twin](/tools/lyogavin-airllm/alternatives.md), [codellama markdown twin](/tools/meta-llama-codellama/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-meta-llama-codellama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, airllm or codellama? airllm: Very active. codellama: Archived. 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 codellama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [airllm trust report](/tools/lyogavin-airllm/trust); [codellama trust report](/tools/meta-llama-codellama/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/_