--- title: "cactus vs airllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/cactus-compute-cactus-vs-lyogavin-airllm" tools: ["cactus-compute-cactus", "lyogavin-airllm"] --- # cactus vs airllm *GraphCanon updated Sep 20, 2026* ## Verdict Pick cactus if cactus - Low-latency AI engine optimized for mobile and wearable devices; pick airllm if airLLM is a repository that enables the inference of a 70B parameter model using a single 4GB GPU, with a focus on Chinese language models and generative AI. [cactus](https://cactuscompute.com) reports 6.0k GitHub stars, 505 forks, and 103 open issues, last pushed Sep 8, 2026. [airllm](https://github.com/lyogavin/airllm) has 35k stars, 3.6k forks, and 155 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [cactus's repository](https://github.com/cactus-compute/cactus) and [airllm's repository](https://github.com/lyogavin/airllm). | | [cactus](/tools/cactus-compute-cactus.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Tagline | Low-latency AI engine for mobile devices & wearables | Inference for AirLLM 70B model with single 4GB GPU | | Stars | 6,030 | 34,504 | | Forks | 505 | 3,626 | | Open issues | 103 | 155 | | Language | C++ | Jupyter Notebook | | Adopt for | Cactus - Low-latency AI engine optimized for mobile and wearable devices. | AirLLM is a repository that enables the inference of a 70B parameter model using a single 4GB GPU, with a focus on Chinese language models and generative AI. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | Inference & Serving, Speech & Audio | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [cactus](/tools/cactus-compute-cactus.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 11d | 0d | | Open issues (now) | 103 | 155 | | Stars delta | +495 (30d) | +10k (30d) | | Open issues delta | +21 (30d) | +40 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/cactus-compute-cactus/trust.md) | [trust report](/tools/lyogavin-airllm/trust.md) | ## Decision facts: cactus - **Pricing:** unknown - **Adopt for:** Cactus - Low-latency AI engine optimized for mobile and wearable devices. - **License detail:** Other ## Decision facts: airllm - **Requirements:** Min 4 GB RAM; Installation is straightforward via pip. - **Adopt for:** AirLLM is a repository that enables the inference of a 70B parameter model using a single 4GB GPU, with a focus on Chinese language models and generative AI. - **License detail:** Apache-2.0 ## Choose when ### Choose cactus if… - cactus is primarily C++; airllm is Jupyter Notebook. - License: cactus is Other, airllm is Apache-2.0. - Tags unique to cactus: ai, android, arm, edge. - Also covers Speech & Audio. - - When you need fast response times on mobile or wearable devices for tasks like speech recognition and general inference. ### Choose airllm if… - airllm is primarily Jupyter Notebook; cactus is C++. - License: airllm is Apache-2.0, cactus is Other. - Requirements: Min 4 GB RAM; Installation is straightforward via pip.. - Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai. - Also covers LLM Frameworks. - When you need to perform inference on a 70B parameter model with limited GPU resources, such as a single 4GB GPU. ## When NOT to use cactus - - In situations that require high-complexity AI applications beyond general inference, such as detailed image segmentation or extensive natural language understanding tasks. - - When working with desktop or server environments, as Cactus is specifically optimized for mobile and wearable hardware constraints. ## When NOT to use airllm - If your project requires inference on models larger than 70B parameters or if you have access to more powerful GPUs than a 4GB model. - For projects that do not involve Chinese language models or generative AI tasks, as AirLLM is specifically optimized for these areas. ## Common questions ### What is the difference between cactus and airllm? cactus: Low-latency AI engine for mobile devices & wearables. airllm: Inference for AirLLM 70B model with single 4GB GPU. See the comparison table for live GitHub stats and shared categories. ### When should I choose cactus over airllm? Choose cactus over airllm when cactus is primarily C++; airllm is Jupyter Notebook; License: cactus is Other, airllm is Apache-2.0; Tags unique to cactus: ai, android, arm, edge; Also covers Speech & Audio; - When you need fast response times on mobile or wearable devices for tasks like speech recognition and general inference. ### When should I choose airllm over cactus? Choose airllm over cactus when airllm is primarily Jupyter Notebook; cactus is C++; License: airllm is Apache-2.0, cactus is Other; Requirements: Min 4 GB RAM; Installation is straightforward via pip.; Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai; Also covers LLM Frameworks; When you need to perform inference on a 70B parameter model with limited GPU resources, such as a single 4GB GPU. ### When should I avoid cactus? - In situations that require high-complexity AI applications beyond general inference, such as detailed image segmentation or extensive natural language understanding tasks. - When working with desktop or server environments, as Cactus is specifically optimized for mobile and wearable hardware constraints. ### When should I avoid airllm? If your project requires inference on models larger than 70B parameters or if you have access to more powerful GPUs than a 4GB model. For projects that do not involve Chinese language models or generative AI tasks, as AirLLM is specifically optimized for these areas. ### Is cactus or airllm more popular on GitHub? airllm has more GitHub stars (34,504 vs 6,030). Stars measure visibility, not whether either tool fits your constraints. ### Are cactus and airllm open source? Yes - both are open-source projects on GitHub (cactus: Other, airllm: Apache-2.0). ### Where can I find alternatives to cactus or airllm? GraphCanon lists graph-backed alternatives at [cactus alternatives](/tools/cactus-compute-cactus/alternatives) and [airllm alternatives](/tools/lyogavin-airllm/alternatives) ([cactus markdown twin](/tools/cactus-compute-cactus/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/cactus-compute-cactus-vs-lyogavin-airllm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, cactus or airllm? cactus: Active. 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 cactus and airllm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [cactus trust report](/tools/cactus-compute-cactus/trust); [airllm trust report](/tools/lyogavin-airllm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=cactus-compute-cactus`](/api/graphcanon/graph?tool=cactus-compute-cactus) - 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/_