--- title: "FlexLLMGen vs airllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fminference-flexllmgen-vs-lyogavin-airllm" tools: ["fminference-flexllmgen", "lyogavin-airllm"] --- # FlexLLMGen vs airllm *GraphCanon updated Jul 12, 2026* ## Verdict Pick FlexLLMGen if flexLLMGen runs large language models efficiently on a single GPU, ideal for throughput-oriented tasks thanks to its intelligent offloading capabilities; 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. [FlexLLMGen](https://github.com/FMInference/FlexLLMGen) reports 9.4k GitHub stars, 589 forks, and 58 open issues, last pushed Oct 28, 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 [FlexLLMGen's repository](https://github.com/FMInference/FlexLLMGen) and [airllm's repository](https://github.com/lyogavin/airllm). | | [FlexLLMGen](/tools/fminference-flexllmgen.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Tagline | Running large language models on a single GPU for throughput-oriented scenarios. | AirLLM 70B inference with single 4GB GPU | | Stars | 9,361 | 22,399 | | Forks | 589 | 2,581 | | Open issues | 58 | 106 | | Language | Python | Jupyter Notebook | | Adopt for | FlexLLMGen runs large language models efficiently on a single GPU, ideal for throughput-oriented tasks thanks to its intelligent offloading capabilities. | 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._ | | [FlexLLMGen](/tools/fminference-flexllmgen.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Very active (96%) | | Days since push | 621d | 0d | | Archived on GitHub | Yes | No | | Open issues (now) | 58 | 106 | | Owner type | Organization | User | | Security scan | No lockfile | 4 low (4 low) | | Full report | [trust report](/tools/fminference-flexllmgen/trust.md) | [trust report](/tools/lyogavin-airllm/trust.md) | ## Decision facts: FlexLLMGen - **Adopt for:** FlexLLMGen runs large language models efficiently on a single GPU, ideal for throughput-oriented tasks thanks to its intelligent offloading capabilities. ## 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 FlexLLMGen if… - FlexLLMGen is primarily Python; airllm is Jupyter Notebook. - Tags unique to FlexLLMGen: gpt-3, high-throughput, deep-learning, machine-learning. - You need high-throughput inference where tasks can benefit from efficient offloading techniques. ### Choose airllm if… - airllm is primarily Jupyter Notebook; FlexLLMGen 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 FlexLLMGen - The scenario requires distributed computing across multiple GPUs, as FlexLLMGen focuses on optimizing usage of a single GPU. - If your applications demand lower latency rather than high throughput, another tool might be more suitable since FlexLLMGen prioritizes throughput over latency. ## 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 FlexLLMGen and airllm? FlexLLMGen: Running large language models on a single GPU for throughput-oriented scenarios.. airllm: AirLLM 70B inference with single 4GB GPU. See the comparison table for live GitHub stats and shared categories. ### When should I choose FlexLLMGen over airllm? Choose FlexLLMGen over airllm when FlexLLMGen is primarily Python; airllm is Jupyter Notebook; Tags unique to FlexLLMGen: gpt-3, high-throughput, deep-learning, machine-learning; You need high-throughput inference where tasks can benefit from efficient offloading techniques. ### When should I choose airllm over FlexLLMGen? Choose airllm over FlexLLMGen when airllm is primarily Jupyter Notebook; FlexLLMGen 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 FlexLLMGen? The scenario requires distributed computing across multiple GPUs, as FlexLLMGen focuses on optimizing usage of a single GPU. If your applications demand lower latency rather than high throughput, another tool might be more suitable since FlexLLMGen prioritizes throughput over latency. ### 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 FlexLLMGen or airllm more popular on GitHub? airllm has more GitHub stars (22,399 vs 9,361). Stars measure visibility, not whether either tool fits your constraints. ### Are FlexLLMGen and airllm open source? Yes - both are open-source projects on GitHub (FlexLLMGen: Apache-2.0, airllm: Apache-2.0). ### Where can I find alternatives to FlexLLMGen or airllm? GraphCanon lists graph-backed alternatives at [FlexLLMGen alternatives](/tools/fminference-flexllmgen/alternatives) and [airllm alternatives](/tools/lyogavin-airllm/alternatives) ([FlexLLMGen markdown twin](/tools/fminference-flexllmgen/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/fminference-flexllmgen-vs-lyogavin-airllm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FlexLLMGen or airllm? FlexLLMGen: Archived. 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 FlexLLMGen and airllm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FlexLLMGen trust report](/tools/fminference-flexllmgen/trust); [airllm trust report](/tools/lyogavin-airllm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fminference-flexllmgen`](/api/graphcanon/graph?tool=fminference-flexllmgen) - 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/_