--- title: "yalm vs airllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/andrewkchan-yalm-vs-lyogavin-airllm" tools: ["andrewkchan-yalm", "lyogavin-airllm"] --- # yalm vs airllm *GraphCanon updated Aug 25, 2026* ## Verdict Pick yalm if yALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries; 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. [yalm](https://github.com/andrewkchan/yalm) reports 596 GitHub stars, 64 forks, and 4 open issues, last pushed Sep 13, 2025. [airllm](https://github.com/lyogavin/airllm) has 24k stars, 2.7k forks, and 115 open issues, last pushed Jul 23, 2026. Figures are from public GitHub metadata via [yalm's repository](https://github.com/andrewkchan/yalm) and [airllm's repository](https://github.com/lyogavin/airllm). | | [yalm](/tools/andrewkchan-yalm.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Tagline | LLM inference engine in C++/CUDA without dependency on external libraries except for I/O | AirLLM 70B inference with single 4GB GPU | | Stars | 596 | 24,183 | | Forks | 64 | 2,722 | | Open issues | 4 | 115 | | Language | C++ | Jupyter Notebook | | Adopt for | YALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries. | 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 | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [yalm](/tools/andrewkchan-yalm.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 345d | 5d | | Open issues (now) | 4 | 115 | | Stars delta | +4 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/andrewkchan-yalm/trust.md) | [trust report](/tools/lyogavin-airllm/trust.md) | ## Decision facts: yalm - **Adopt for:** YALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries. ## 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 yalm if… - yalm is primarily C++; airllm is Jupyter Notebook. - Tags unique to yalm: cpp, cuda, llm-inference, machine-learning. - When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies ### Choose airllm if… - airllm is primarily Jupyter Notebook; yalm is C++. - 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 NOT to use yalm - If extensive functionality or ease of use from other ML libraries is required, as YALM does not support dependencies beyond I/O needs - For developers who prefer tools with broader community support and more comprehensive feature sets, given that YALM specializes in a narrow scope ## 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 yalm and airllm? yalm: LLM inference engine in C++/CUDA without dependency on external libraries except for I/O. airllm: AirLLM 70B inference with single 4GB GPU. See the comparison table for live GitHub stats and shared categories. ### When should I choose yalm over airllm? Choose yalm over airllm when yalm is primarily C++; airllm is Jupyter Notebook; Tags unique to yalm: cpp, cuda, llm-inference, machine-learning; When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies. ### When should I choose airllm over yalm? Choose airllm over yalm when airllm is primarily Jupyter Notebook; yalm is C++; 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 avoid yalm? If extensive functionality or ease of use from other ML libraries is required, as YALM does not support dependencies beyond I/O needs For developers who prefer tools with broader community support and more comprehensive feature sets, given that YALM specializes in a narrow scope ### 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 yalm or airllm more popular on GitHub? airllm has more GitHub stars (24,183 vs 596). Stars measure visibility, not whether either tool fits your constraints. ### Are yalm and airllm open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to yalm or airllm? GraphCanon lists graph-backed alternatives at [yalm alternatives](/tools/andrewkchan-yalm/alternatives) and [airllm alternatives](/tools/lyogavin-airllm/alternatives) ([yalm markdown twin](/tools/andrewkchan-yalm/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/andrewkchan-yalm-vs-lyogavin-airllm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, yalm or airllm? yalm: Slowing. 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 yalm and airllm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [yalm trust report](/tools/andrewkchan-yalm/trust); [airllm trust report](/tools/lyogavin-airllm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=andrewkchan-yalm`](/api/graphcanon/graph?tool=andrewkchan-yalm) - 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/_