--- title: "Awesome-LLM-Compression vs Eagle" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huangowen-awesome-llm-compression-vs-nvlabs-eagle" tools: ["huangowen-awesome-llm-compression", "nvlabs-eagle"] --- # Awesome-LLM-Compression vs Eagle *GraphCanon updated Aug 18, 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 Eagle if eagle: Frontier Vision-Language Models with Data-Centric Strategies. [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. [Eagle](https://nvlabs.github.io/Eagle/) has 3.4k stars, 327 forks, and 62 open issues, last pushed Jun 24, 2026. Figures are from public GitHub metadata via [Awesome-LLM-Compression's repository](https://github.com/HuangOwen/Awesome-LLM-Compression) and [Eagle's repository](https://github.com/NVlabs/Eagle). | | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | [Eagle](/tools/nvlabs-eagle.md) | | --- | --- | --- | | Tagline | Awesome LLM compression research papers and tools to accelerate LLM training and inference. | Frontier Vision-Language Models with Data-Centric Strategies | | Stars | 1,859 | 3,407 | | Forks | 129 | 327 | | Open issues | 1 | 62 | | 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. | Eagle: Frontier Vision-Language Models with Data-Centric Strategies | | Persona | - | - | | Runtime | - | - | | License | MIT License | The code is released under Apache 2.0 license, while the pretrained models are under CC BY-NC 4.0 or NVIDIA licenses for non-commercial use only. | | Categories | Inference & Serving, LLM Frameworks | Computer Vision, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | [Eagle](/tools/nvlabs-eagle.md) | | --- | --- | --- | | Days since push | 37d | 54d | | Open issues (now) | 1 | 62 | | Stars delta | Unknown | +199 (30d) | | Open issues delta | Unknown | +3 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/huangowen-awesome-llm-compression/trust.md) | [trust report](/tools/nvlabs-eagle/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: Eagle - **Pricing:** freemium - Free for non-commercial use; requires adherence to licensing agreements - **Requirements:** Min 8 GB RAM; Ensure compliance with all applicable laws and regulations when using the dataset and model weights. - **Adopt for:** Eagle: Frontier Vision-Language Models with Data-Centric Strategies - **License detail:** The code is released under Apache 2.0 license, while the pretrained models are under CC BY-NC 4.0 or NVIDIA licenses for non-commercial use only. ## Choose when ### Choose Awesome-LLM-Compression if… - License: Awesome-LLM-Compression is MIT, Eagle 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 Inference & Serving. - When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference. ### Choose Eagle if… - License: Eagle is Apache-2.0, Awesome-LLM-Compression is MIT. - Pricing: Free for non-commercial use; requires adherence to licensing agreements. - Requirements: Min 8 GB RAM; Ensure compliance with all applicable laws and regulations when using the dataset and model weights.. - Tags unique to Eagle: data-centric-strategies, gpt4, huggingface, llm-improvements. - Also covers Computer Vision. - When you need advanced vision-language models enhanced by data-centric strategies developed by NVlabs and improved using Qwen. ## 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 Eagle - If your project requires commercial use, as Eagle's models are intended for non-commercial use only under the CC BY-NC 4.0 License or NVIDIA License. - In situations where you require a vision-language model that does not rely on improvements made using Qwen. ## Common questions ### What is the difference between Awesome-LLM-Compression and Eagle? Awesome-LLM-Compression: Awesome LLM compression research papers and tools to accelerate LLM training and inference.. Eagle: Frontier Vision-Language Models with Data-Centric Strategies. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLM-Compression over Eagle? Choose Awesome-LLM-Compression over Eagle when License: Awesome-LLM-Compression is MIT, Eagle 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 Inference & Serving; When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference. ### When should I choose Eagle over Awesome-LLM-Compression? Choose Eagle over Awesome-LLM-Compression when License: Eagle is Apache-2.0, Awesome-LLM-Compression is MIT; Pricing: Free for non-commercial use; requires adherence to licensing agreements; Requirements: Min 8 GB RAM; Ensure compliance with all applicable laws and regulations when using the dataset and model weights.; Tags unique to Eagle: data-centric-strategies, gpt4, huggingface, llm-improvements; Also covers Computer Vision; When you need advanced vision-language models enhanced by data-centric strategies developed by NVlabs and improved using Qwen. ### 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 Eagle? If your project requires commercial use, as Eagle's models are intended for non-commercial use only under the CC BY-NC 4.0 License or NVIDIA License. In situations where you require a vision-language model that does not rely on improvements made using Qwen. ### Is Awesome-LLM-Compression or Eagle more popular on GitHub? Eagle has more GitHub stars (3,407 vs 1,859). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLM-Compression and Eagle open source? Yes - both are open-source projects on GitHub (Awesome-LLM-Compression: MIT, Eagle: Apache-2.0). ### Where can I find alternatives to Awesome-LLM-Compression or Eagle? GraphCanon lists graph-backed alternatives at [Awesome-LLM-Compression alternatives](/tools/huangowen-awesome-llm-compression/alternatives) and [Eagle alternatives](/tools/nvlabs-eagle/alternatives) ([Awesome-LLM-Compression markdown twin](/tools/huangowen-awesome-llm-compression/alternatives.md), [Eagle markdown twin](/tools/nvlabs-eagle/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-nvlabs-eagle.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 Eagle? Awesome-LLM-Compression: Steady. Eagle: Steady. 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 Eagle? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLM-Compression trust report](/tools/huangowen-awesome-llm-compression/trust); [Eagle trust report](/tools/nvlabs-eagle/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/_