--- title: "lmdeploy vs airllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/internlm-lmdeploy-vs-lyogavin-airllm" tools: ["internlm-lmdeploy", "lyogavin-airllm"] --- # lmdeploy vs airllm *GraphCanon updated Aug 7, 2026* ## Verdict Pick lmdeploy if lMDeploy is focused on compressing and efficiently serving LLMs, making it suitable for teams already invested in CUDA environments like Nvidia's GeForce RTX 50 series; 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. [lmdeploy](https://lmdeploy.readthedocs.io/en/latest) reports 8.0k GitHub stars, 723 forks, and 607 open issues, last pushed Aug 6, 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 [lmdeploy's repository](https://github.com/InternLM/lmdeploy) and [airllm's repository](https://github.com/lyogavin/airllm). | | [lmdeploy](/tools/internlm-lmdeploy.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Tagline | Toolkit for compressing, deploying, and serving LLMs | AirLLM 70B inference with single 4GB GPU | | Stars | 7,995 | 24,183 | | Forks | 723 | 2,722 | | Open issues | 607 | 115 | | Language | Python | Jupyter Notebook | | Adopt for | LMDeploy is focused on compressing and efficiently serving LLMs, making it suitable for teams already invested in CUDA environments like Nvidia's GeForce RTX 50 series. | 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 | Licensed under Apache-2.0, enabling flexible use and modification for both commercial and open-source projects, provided that users comply with its terms. | Apache-2.0 | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lmdeploy](/tools/internlm-lmdeploy.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Days since push | 1d | 5d | | Open issues (now) | 607 | 115 | | Owner type | Organization | User | | Full report | [trust report](/tools/internlm-lmdeploy/trust.md) | [trust report](/tools/lyogavin-airllm/trust.md) | ## Shared compatibility - **Python**: [lmdeploy](/tools/internlm-lmdeploy.md) - Python runtime; [airllm](/tools/lyogavin-airllm.md) - Python runtime ## Decision facts: lmdeploy - **Requirements:** Installation is optimized through pip in a Conda environment using Python versions between 3.10 and 3.13. - **Adopt for:** LMDeploy is focused on compressing and efficiently serving LLMs, making it suitable for teams already invested in CUDA environments like Nvidia's GeForce RTX 50 series. - **License detail:** Licensed under Apache-2.0, enabling flexible use and modification for both commercial and open-source projects, provided that users comply with its terms. ## 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 lmdeploy if… - lmdeploy is primarily Python; airllm is Jupyter Notebook. - Requirements: Installation is optimized through pip in a Conda environment using Python versions between 3.10 and 3.13.. - Tags unique to lmdeploy: codellama, cuda-kernels, deepspeed, fastertransformer. - When your team operates within a CUDA environment, such as using an Nvidia GeForce RTX 50 series GPU, because the default prebuilt wheels are optimized for CUDA 12.8. ### Choose airllm if… - airllm is primarily Jupyter Notebook; lmdeploy 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: 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 lmdeploy - When your infrastructure relies on software environments or GPUs not aligned with CUDA 12.8, as LMDeploy's default prebuilt wheels might require adjustments to operate optimally. - If you are working exclusively in non-Nvidia GPU ecosystems where LMDeploy's CUDA focus does not align with the hardware optimizations available. ## 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 lmdeploy and airllm? lmdeploy: Toolkit for compressing, deploying, and serving LLMs. airllm: AirLLM 70B inference with single 4GB GPU. See the comparison table for live GitHub stats and shared categories. ### When should I choose lmdeploy over airllm? Choose lmdeploy over airllm when lmdeploy is primarily Python; airllm is Jupyter Notebook; Requirements: Installation is optimized through pip in a Conda environment using Python versions between 3.10 and 3.13.; Tags unique to lmdeploy: codellama, cuda-kernels, deepspeed, fastertransformer; When your team operates within a CUDA environment, such as using an Nvidia GeForce RTX 50 series GPU, because the default prebuilt wheels are optimized for CUDA 12.8. ### When should I choose airllm over lmdeploy? Choose airllm over lmdeploy when airllm is primarily Jupyter Notebook; lmdeploy 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: 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 lmdeploy? When your infrastructure relies on software environments or GPUs not aligned with CUDA 12.8, as LMDeploy's default prebuilt wheels might require adjustments to operate optimally. If you are working exclusively in non-Nvidia GPU ecosystems where LMDeploy's CUDA focus does not align with the hardware optimizations available. ### 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 lmdeploy or airllm more popular on GitHub? airllm has more GitHub stars (24,183 vs 7,995). Stars measure visibility, not whether either tool fits your constraints. ### Are lmdeploy and airllm open source? Yes - both are open-source projects on GitHub (lmdeploy: Apache-2.0, airllm: Apache-2.0). ### Where can I find alternatives to lmdeploy or airllm? GraphCanon lists graph-backed alternatives at [lmdeploy alternatives](/tools/internlm-lmdeploy/alternatives) and [airllm alternatives](/tools/lyogavin-airllm/alternatives) ([lmdeploy markdown twin](/tools/internlm-lmdeploy/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/internlm-lmdeploy-vs-lyogavin-airllm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lmdeploy or airllm? lmdeploy: 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 lmdeploy and airllm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lmdeploy trust report](/tools/internlm-lmdeploy/trust); [airllm trust report](/tools/lyogavin-airllm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=internlm-lmdeploy`](/api/graphcanon/graph?tool=internlm-lmdeploy) - 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/_