--- title: "lmdeploy vs awesome-local-llm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/internlm-lmdeploy-vs-rafska-awesome-local-llm" tools: ["internlm-lmdeploy", "rafska-awesome-local-llm"] --- # lmdeploy vs awesome-local-llm *GraphCanon updated Aug 12, 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 awesome-local-llm if awesome-local-llm is a curated list of resources for the local operation of large language models. [lmdeploy](https://lmdeploy.readthedocs.io/en/latest) reports 8.0k GitHub stars, 723 forks, and 607 open issues, last pushed Aug 6, 2026. [awesome-local-llm](https://github.com/rafska/awesome-local-llm) has 2.5k stars, 316 forks, and 129 open issues, last pushed Aug 4, 2026. Figures are from public GitHub metadata via [lmdeploy's repository](https://github.com/InternLM/lmdeploy) and [awesome-local-llm's repository](https://github.com/rafska/awesome-local-llm). | | [lmdeploy](/tools/internlm-lmdeploy.md) | [awesome-local-llm](/tools/rafska-awesome-local-llm.md) | | --- | --- | --- | | Tagline | Toolkit for compressing, deploying, and serving LLMs | Resources for running LLMs locally | | Stars | 7,995 | 2,518 | | Forks | 723 | 316 | | Open issues | 607 | 129 | | Language | Python | - | | 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. | awesome-local-llm is a curated list of resources for the local operation of large language models. | | 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. | MIT License | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lmdeploy](/tools/internlm-lmdeploy.md) | [awesome-local-llm](/tools/rafska-awesome-local-llm.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 7d | | Open issues (now) | 607 | 129 | | Owner type | Organization | User | | Full report | [trust report](/tools/internlm-lmdeploy/trust.md) | [trust report](/tools/rafska-awesome-local-llm/trust.md) | ## 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: awesome-local-llm - **Pricing:** freemium - The list itself is free and open-source under the MIT license. - **Requirements:** Technical skill in setting up a self-hosted large language model environment is necessary - **Adopt for:** awesome-local-llm is a curated list of resources for the local operation of large language models. - **License detail:** MIT License ## Choose when ### Choose lmdeploy if… - License: lmdeploy is Apache-2.0, awesome-local-llm is MIT. - 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 awesome-local-llm if… - License: awesome-local-llm is MIT, lmdeploy is Apache-2.0. - Pricing: The list itself is free and open-source under the MIT license.. - Requirements: Technical skill in setting up a self-hosted large language model environment is necessary. - Tags unique to awesome-local-llm: ai, awesome-list, llm, local-ai. - - If you require extensive documentation and resources for setting up and running LLMs on your own hardware, this tool provides a comprehensive list of options ## 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 awesome-local-llm - - Avoid if you seek direct tools rather than a curated list; awesome-local-llm does not provide the actual software but guidance and links - - Not suitable for users who prefer ready-to-use solutions without needing additional configuration, as it requires self-hosting expertise to utilize its resources ## Common questions ### What is the difference between lmdeploy and awesome-local-llm? lmdeploy: Toolkit for compressing, deploying, and serving LLMs. awesome-local-llm: Resources for running LLMs locally. See the comparison table for live GitHub stats and shared categories. ### When should I choose lmdeploy over awesome-local-llm? Choose lmdeploy over awesome-local-llm when License: lmdeploy is Apache-2.0, awesome-local-llm is MIT; 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 awesome-local-llm over lmdeploy? Choose awesome-local-llm over lmdeploy when License: awesome-local-llm is MIT, lmdeploy is Apache-2.0; Pricing: The list itself is free and open-source under the MIT license.; Requirements: Technical skill in setting up a self-hosted large language model environment is necessary; Tags unique to awesome-local-llm: ai, awesome-list, llm, local-ai; - If you require extensive documentation and resources for setting up and running LLMs on your own hardware, this tool provides a comprehensive list of options. ### 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 awesome-local-llm? - Avoid if you seek direct tools rather than a curated list; awesome-local-llm does not provide the actual software but guidance and links - Not suitable for users who prefer ready-to-use solutions without needing additional configuration, as it requires self-hosting expertise to utilize its resources ### Is lmdeploy or awesome-local-llm more popular on GitHub? lmdeploy has more GitHub stars (7,995 vs 2,518). Stars measure visibility, not whether either tool fits your constraints. ### Are lmdeploy and awesome-local-llm open source? Yes - both are open-source projects on GitHub (lmdeploy: Apache-2.0, awesome-local-llm: MIT). ### Where can I find alternatives to lmdeploy or awesome-local-llm? GraphCanon lists graph-backed alternatives at [lmdeploy alternatives](/tools/internlm-lmdeploy/alternatives) and [awesome-local-llm alternatives](/tools/rafska-awesome-local-llm/alternatives) ([lmdeploy markdown twin](/tools/internlm-lmdeploy/alternatives.md), [awesome-local-llm markdown twin](/tools/rafska-awesome-local-llm/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-rafska-awesome-local-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lmdeploy or awesome-local-llm? lmdeploy: Very active. awesome-local-llm: 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 awesome-local-llm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lmdeploy trust report](/tools/internlm-lmdeploy/trust); [awesome-local-llm trust report](/tools/rafska-awesome-local-llm/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/_