--- title: "Awesome-LLM-Compression vs lmdeploy" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huangowen-awesome-llm-compression-vs-internlm-lmdeploy" tools: ["huangowen-awesome-llm-compression", "internlm-lmdeploy"] --- # Awesome-LLM-Compression vs lmdeploy *GraphCanon updated Aug 7, 2026* ## Verdict Pick Awesome-LLM-Compression if awesome LLM-Compression curates a comprehensive collection of research papers and tools aimed at compressing large language models, focusing on enhancing computational efficiency during both training and serving phases; 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. [Awesome-LLM-Compression](https://github.com/HuangOwen/Awesome-LLM-Compression) reports 1.9k GitHub stars, 129 forks, and 1 open issues, last pushed Jun 30, 2026. [lmdeploy](https://lmdeploy.readthedocs.io/en/latest) has 8.0k stars, 723 forks, and 607 open issues, last pushed Aug 6, 2026. Figures are from public GitHub metadata via [Awesome-LLM-Compression's repository](https://github.com/HuangOwen/Awesome-LLM-Compression) and [lmdeploy's repository](https://github.com/InternLM/lmdeploy). | | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | [lmdeploy](/tools/internlm-lmdeploy.md) | | --- | --- | --- | | Tagline | Awesome LLM compression research papers and tools to accelerate LLM training and inference. | Toolkit for compressing, deploying, and serving LLMs | | Stars | 1,859 | 7,995 | | Forks | 129 | 723 | | Open issues | 1 | 607 | | Language | - | Python | | Adopt for | Awesome LLM-Compression curates a comprehensive collection of research papers and tools aimed at compressing large language models, focusing on enhancing computational efficiency during both training and serving phases. | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT 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. | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | [lmdeploy](/tools/internlm-lmdeploy.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 37d | 1d | | Open issues (now) | 1 | 607 | | Owner type | User | Organization | | Full report | [trust report](/tools/huangowen-awesome-llm-compression/trust.md) | [trust report](/tools/internlm-lmdeploy/trust.md) | ## Decision facts: Awesome-LLM-Compression - **Requirements:** The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable. - **Adopt for:** Awesome LLM-Compression curates a comprehensive collection of research papers and tools aimed at compressing large language models, focusing on enhancing computational efficiency during both training and serving phases. - **License detail:** MIT License ## 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. ## Choose when ### Choose Awesome-LLM-Compression if… - License: Awesome-LLM-Compression is MIT, lmdeploy is Apache-2.0. - Requirements: The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable.. - Tags unique to Awesome-LLM-Compression: compression, efficiency, research papers, training acceleration. - Also covers LLM Frameworks. - When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference. ### Choose lmdeploy if… - License: lmdeploy is Apache-2.0, Awesome-LLM-Compression 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 NOT to use Awesome-LLM-Compression - Avoid relying solely on Awesome LLM-Compression if you require a hands-on toolset rather than theoretical frameworks and research papers, as it focuses more on consolidating the survey information. - If your immediate need is for proprietary or commercial tools that offer out-of-the-box functionality, since this resource mainly links to academic research and open-source projects. ## 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. ## Common questions ### What is the difference between Awesome-LLM-Compression and lmdeploy? Awesome-LLM-Compression: Awesome LLM compression research papers and tools to accelerate LLM training and inference.. lmdeploy: Toolkit for compressing, deploying, and serving LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLM-Compression over lmdeploy? Choose Awesome-LLM-Compression over lmdeploy when License: Awesome-LLM-Compression is MIT, lmdeploy is Apache-2.0; Requirements: The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable.; Tags unique to Awesome-LLM-Compression: compression, efficiency, research papers, training acceleration; Also covers LLM Frameworks; When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference. ### When should I choose lmdeploy over Awesome-LLM-Compression? Choose lmdeploy over Awesome-LLM-Compression when License: lmdeploy is Apache-2.0, Awesome-LLM-Compression 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 avoid Awesome-LLM-Compression? Avoid relying solely on Awesome LLM-Compression if you require a hands-on toolset rather than theoretical frameworks and research papers, as it focuses more on consolidating the survey information. If your immediate need is for proprietary or commercial tools that offer out-of-the-box functionality, since this resource mainly links to academic research and open-source projects. ### 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. ### Is Awesome-LLM-Compression or lmdeploy more popular on GitHub? lmdeploy has more GitHub stars (7,995 vs 1,859). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLM-Compression and lmdeploy open source? Yes - both are open-source projects on GitHub (Awesome-LLM-Compression: MIT, lmdeploy: Apache-2.0). ### Where can I find alternatives to Awesome-LLM-Compression or lmdeploy? GraphCanon lists graph-backed alternatives at [Awesome-LLM-Compression alternatives](/tools/huangowen-awesome-llm-compression/alternatives) and [lmdeploy alternatives](/tools/internlm-lmdeploy/alternatives) ([Awesome-LLM-Compression markdown twin](/tools/huangowen-awesome-llm-compression/alternatives.md), [lmdeploy markdown twin](/tools/internlm-lmdeploy/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/huangowen-awesome-llm-compression-vs-internlm-lmdeploy.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLM-Compression or lmdeploy? Awesome-LLM-Compression: Steady. lmdeploy: 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 Awesome-LLM-Compression and lmdeploy? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLM-Compression trust report](/tools/huangowen-awesome-llm-compression/trust); [lmdeploy trust report](/tools/internlm-lmdeploy/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huangowen-awesome-llm-compression`](/api/graphcanon/graph?tool=huangowen-awesome-llm-compression) - 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/_