--- title: "airllm vs TNN" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lyogavin-airllm-vs-tencent-tnn" tools: ["lyogavin-airllm", "tencent-tnn"] --- # airllm vs TNN *GraphCanon updated Aug 4, 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 TNN if developed by Tencent Labs, TNN offers strong cross-platform performance with efficient model compression and runtime optimization for mobile to server use. [airllm](https://github.com/lyogavin/airllm) reports 24k GitHub stars, 2.7k forks, and 115 open issues, last pushed Jul 23, 2026. [TNN](https://github.com/Tencent/TNN) has 4.6k stars, 772 forks, and 318 open issues, last pushed May 9, 2025. Figures are from public GitHub metadata via [airllm's repository](https://github.com/lyogavin/airllm) and [TNN's repository](https://github.com/Tencent/TNN). | | [airllm](/tools/lyogavin-airllm.md) | [TNN](/tools/tencent-tnn.md) | | --- | --- | --- | | Tagline | AirLLM 70B inference with single 4GB GPU | A cross-platform deep learning inference framework for diverse computing environments, from mobile to desktop and server. | | Stars | 24,183 | 4,643 | | Forks | 2,722 | 772 | | Open issues | 115 | 318 | | Language | Jupyter Notebook | C++ | | Adopt for | AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU. | Developed by Tencent Labs, TNN offers strong cross-platform performance with efficient model compression and runtime optimization for mobile to server use. | | 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) | [TNN](/tools/tencent-tnn.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 5d | 452d | | Open issues (now) | 115 | 318 | | Owner type | User | Organization | | Full report | [trust report](/tools/lyogavin-airllm/trust.md) | [trust report](/tools/tencent-tnn/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: TNN - **Adopt for:** Developed by Tencent Labs, TNN offers strong cross-platform performance with efficient model compression and runtime optimization for mobile to server use. ## Choose when ### Choose airllm if… - airllm is primarily Jupyter Notebook; TNN is C++. - License: airllm is Apache-2.0, TNN 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 TNN if… - TNN is primarily C++; airllm is Jupyter Notebook. - License: TNN is Other, airllm is Apache-2.0. - Tags unique to TNN: coreml, deep-learning, face-detection, hairsegmentaion. - TNN ships Docker support for self-hosted deployment. - When developing AI apps for Tencent-affiliated software like Mobile QQ or Weishi ## 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 TNN - If you prefer a framework that heavily integrates with TensorFlow's ecosystem, as TNN has a steeper learning curve when converting models - When your project primarily relies on Python environments. TNN is C++-centric with no native Python interface. ## Common questions ### What is the difference between airllm and TNN? airllm: AirLLM 70B inference with single 4GB GPU. TNN: A cross-platform deep learning inference framework for diverse computing environments, from mobile to desktop and server.. See the comparison table for live GitHub stats and shared categories. ### When should I choose airllm over TNN? Choose airllm over TNN when airllm is primarily Jupyter Notebook; TNN is C++; License: airllm is Apache-2.0, TNN 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 TNN over airllm? Choose TNN over airllm when TNN is primarily C++; airllm is Jupyter Notebook; License: TNN is Other, airllm is Apache-2.0; Tags unique to TNN: coreml, deep-learning, face-detection, hairsegmentaion; TNN ships Docker support for self-hosted deployment; When developing AI apps for Tencent-affiliated software like Mobile QQ or Weishi. ### 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 TNN? If you prefer a framework that heavily integrates with TensorFlow's ecosystem, as TNN has a steeper learning curve when converting models When your project primarily relies on Python environments. TNN is C++-centric with no native Python interface. ### Is airllm or TNN more popular on GitHub? airllm has more GitHub stars (24,183 vs 4,643). Stars measure visibility, not whether either tool fits your constraints. ### Are airllm and TNN open source? Yes - both are open-source projects on GitHub (airllm: Apache-2.0, TNN: Other). ### Where can I find alternatives to airllm or TNN? GraphCanon lists graph-backed alternatives at [airllm alternatives](/tools/lyogavin-airllm/alternatives) and [TNN alternatives](/tools/tencent-tnn/alternatives) ([airllm markdown twin](/tools/lyogavin-airllm/alternatives.md), [TNN markdown twin](/tools/tencent-tnn/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-tencent-tnn.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, airllm or TNN? airllm: Very active. TNN: Dormant. 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 TNN? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [airllm trust report](/tools/lyogavin-airllm/trust); [TNN trust report](/tools/tencent-tnn/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/_