--- title: "airllm vs seldon-core" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lyogavin-airllm-vs-seldonio-seldon-core" tools: ["lyogavin-airllm", "seldonio-seldon-core"] --- # airllm vs seldon-core *GraphCanon updated Jul 11, 2026* ## Verdict Pick airllm when airllm is primarily Jupyter Notebook; seldon-core is Go; pick seldon-core when seldon-core is primarily Go; 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. [seldon-core](https://www.seldon.io/solutions/core/) has 4.8k stars, 865 forks, and 394 open issues, last pushed Mar 23, 2026. Figures are from public GitHub metadata via [airllm's repository](https://github.com/lyogavin/airllm) and [seldon-core's repository](https://github.com/SeldonIO/seldon-core). | | [airllm](/tools/lyogavin-airllm.md) | [seldon-core](/tools/seldonio-seldon-core.md) | | --- | --- | --- | | Tagline | AirLLM 70B inference with single 4GB GPU | An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models | | Stars | 22,399 | 4,759 | | Forks | 2,581 | 865 | | Open issues | 106 | 394 | | Language | Jupyter Notebook | Go | | 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) | [seldon-core](/tools/seldonio-seldon-core.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 110d | | Open issues (now) | 106 | 394 | | Owner type | User | Organization | | Security scan | 4 low (4 low) | No lockfile | | Full report | [trust report](/tools/lyogavin-airllm/trust.md) | [trust report](/tools/seldonio-seldon-core/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; seldon-core is Go. - License: airllm is Apache-2.0, seldon-core 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: 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 seldon-core if… - seldon-core is primarily Go; airllm is Jupyter Notebook. - License: seldon-core is Other, airllm is Apache-2.0. - Tags unique to seldon-core: aiops, deployment, kubernetes, machine-learning. ## 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 seldon-core - Last GitHub push was 111 days ago (slowing maintenance, Mar 23, 2026). Validate activity before betting a new project on seldon-core. - 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 seldon-core? airllm: AirLLM 70B inference with single 4GB GPU. seldon-core: An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models. See the comparison table for live GitHub stats and shared categories. ### When should I choose airllm over seldon-core? Choose airllm over seldon-core when airllm is primarily Jupyter Notebook; seldon-core is Go; License: airllm is Apache-2.0, seldon-core 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: 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 seldon-core over airllm? Choose seldon-core over airllm when seldon-core is primarily Go; airllm is Jupyter Notebook; License: seldon-core is Other, airllm is Apache-2.0; Tags unique to seldon-core: aiops, deployment, kubernetes, machine-learning. ### 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 seldon-core? Last GitHub push was 111 days ago (slowing maintenance, Mar 23, 2026). Validate activity before betting a new project on seldon-core. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is airllm or seldon-core more popular on GitHub? airllm has more GitHub stars (22,399 vs 4,759). Stars measure visibility, not whether either tool fits your constraints. ### Are airllm and seldon-core open source? Yes - both are open-source projects on GitHub (airllm: Apache-2.0, seldon-core: Other). ### Where can I find alternatives to airllm or seldon-core? GraphCanon lists graph-backed alternatives at [airllm alternatives](/tools/lyogavin-airllm/alternatives) and [seldon-core alternatives](/tools/seldonio-seldon-core/alternatives) ([airllm markdown twin](/tools/lyogavin-airllm/alternatives.md), [seldon-core markdown twin](/tools/seldonio-seldon-core/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-seldonio-seldon-core.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, airllm or seldon-core? airllm: Very active. seldon-core: 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 seldon-core? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [airllm trust report](/tools/lyogavin-airllm/trust); [seldon-core trust report](/tools/seldonio-seldon-core/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/_