--- title: "krasis vs airllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/brontoguana-krasis-vs-lyogavin-airllm" tools: ["brontoguana-krasis", "lyogavin-airllm"] --- # krasis vs airllm *GraphCanon updated Aug 25, 2026* ## Verdict Pick krasis if krasis is designed to offer efficient large model inference on consumer-grade hardware through hybrid CPU-GPU execution and high-performance optimization; 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. [krasis](https://github.com/brontoguana/krasis) reports 516 GitHub stars, 32 forks, and 15 open issues, last pushed Aug 24, 2026. [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 [krasis's repository](https://github.com/brontoguana/krasis) and [airllm's repository](https://github.com/lyogavin/airllm). | | [krasis](/tools/brontoguana-krasis.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Tagline | Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware | AirLLM 70B inference with single 4GB GPU | | Stars | 516 | 24,183 | | Forks | 32 | 2,722 | | Open issues | 15 | 115 | | Language | C++ | Jupyter Notebook | | Adopt for | Krasis is designed to offer efficient large model inference on consumer-grade hardware through hybrid CPU-GPU execution and high-performance optimization. | 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 | Other | Apache-2.0 | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [krasis](/tools/brontoguana-krasis.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Days since push | 1d | 5d | | Open issues (now) | 15 | 115 | | Stars delta | +32 (30d) | Unknown | | Open issues delta | +7 (30d) | Unknown | | Full report | [trust report](/tools/brontoguana-krasis/trust.md) | [trust report](/tools/lyogavin-airllm/trust.md) | ## Shared compatibility - **Python**: [krasis](/tools/brontoguana-krasis.md) - Python runtime; [airllm](/tools/lyogavin-airllm.md) - Python runtime ## Decision facts: krasis - **Adopt for:** Krasis is designed to offer efficient large model inference on consumer-grade hardware through hybrid CPU-GPU execution and high-performance optimization. ## 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 krasis if… - krasis is primarily C++; airllm is Jupyter Notebook. - License: krasis is Other, airllm is Apache-2.0. - Tags unique to krasis: cpu-inference, gguf-model-support, gpu-inference, high-performance-inference. - - When aiming for efficient operation of larger language models with limited VRAM, as Krasis optimizes memory utilization specifically to support this scenario. ### Choose airllm if… - airllm is primarily Jupyter Notebook; krasis is C++. - License: airllm is Apache-2.0, krasis 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 NOT to use krasis - - Avoid using Krasis if your hardware setup does not include both CPU and GPU capabilities, as its hybrid execution relies on utilizing both components for optimal performance. - - If you prioritize running lightweight models with minimal memory footprint on low-end devices, Krasis might not be the ideal choice given it is optimized for larger-scale model inference. ## 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 krasis and airllm? krasis: Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware. airllm: AirLLM 70B inference with single 4GB GPU. See the comparison table for live GitHub stats and shared categories. ### When should I choose krasis over airllm? Choose krasis over airllm when krasis is primarily C++; airllm is Jupyter Notebook; License: krasis is Other, airllm is Apache-2.0; Tags unique to krasis: cpu-inference, gguf-model-support, gpu-inference, high-performance-inference; - When aiming for efficient operation of larger language models with limited VRAM, as Krasis optimizes memory utilization specifically to support this scenario. ### When should I choose airllm over krasis? Choose airllm over krasis when airllm is primarily Jupyter Notebook; krasis is C++; License: airllm is Apache-2.0, krasis 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 avoid krasis? - Avoid using Krasis if your hardware setup does not include both CPU and GPU capabilities, as its hybrid execution relies on utilizing both components for optimal performance. - If you prioritize running lightweight models with minimal memory footprint on low-end devices, Krasis might not be the ideal choice given it is optimized for larger-scale model inference. ### 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 krasis or airllm more popular on GitHub? airllm has more GitHub stars (24,183 vs 516). Stars measure visibility, not whether either tool fits your constraints. ### Are krasis and airllm open source? Yes - both are open-source projects on GitHub (krasis: Other, airllm: Apache-2.0). ### Where can I find alternatives to krasis or airllm? GraphCanon lists graph-backed alternatives at [krasis alternatives](/tools/brontoguana-krasis/alternatives) and [airllm alternatives](/tools/lyogavin-airllm/alternatives) ([krasis markdown twin](/tools/brontoguana-krasis/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/brontoguana-krasis-vs-lyogavin-airllm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, krasis or airllm? krasis: Very 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 krasis and airllm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [krasis trust report](/tools/brontoguana-krasis/trust); [airllm trust report](/tools/lyogavin-airllm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=brontoguana-krasis`](/api/graphcanon/graph?tool=brontoguana-krasis) - 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/_