--- title: "Awesome-LLM-Compression vs EAGLE" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huangowen-awesome-llm-compression-vs-safeailab-eagle" tools: ["huangowen-awesome-llm-compression", "safeailab-eagle"] --- # Awesome-LLM-Compression vs EAGLE *GraphCanon updated Aug 24, 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 offers official implementations for its series of large language models with emphasis on inference and speculative decoding. [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://arxiv.org/pdf/2503.01840) has 2.5k stars, 297 forks, and 101 open issues, last pushed Feb 20, 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/SafeAILab/EAGLE). | | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | [EAGLE](/tools/safeailab-eagle.md) | | --- | --- | --- | | Tagline | Awesome LLM compression research papers and tools to accelerate LLM training and inference. | Official Implementation of EAGLE Series Models | | Stars | 1,859 | 2,510 | | Forks | 129 | 297 | | Open issues | 1 | 101 | | 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 offers official implementations for its series of large language models with emphasis on inference and speculative decoding. | | Persona | - | - | | Runtime | - | - | | License | MIT License | Other | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, 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/safeailab-eagle.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 37d | 155d | | Open issues (now) | 1 | 101 | | Owner type | User | Organization | | Full report | [trust report](/tools/huangowen-awesome-llm-compression/trust.md) | [trust report](/tools/safeailab-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 - **Adopt for:** EAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding. ## Choose when ### Choose Awesome-LLM-Compression if… - License: Awesome-LLM-Compression is MIT, EAGLE is Other. - 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. - 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 Other, Awesome-LLM-Compression is MIT. - Tags unique to EAGLE: large language models, llm-inference, speculative-decoding. - If your project requires the latest advancements in model capabilities from ICML'24, EMNLP'24, and NeurIPS'25 as provided by EAGLE-1, EAGLE-2, or EAGLE-3. ## 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 the specific advancements and techniques implemented in ICML'24 papers are not relevant to your project. - In cases where speculative decoding does not align with the goals or methods of your application, opting for EAGLE may not be beneficial. ## 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: Official Implementation of EAGLE Series Models. 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 Other; 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; 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 Other, Awesome-LLM-Compression is MIT; Tags unique to EAGLE: large language models, llm-inference, speculative-decoding; If your project requires the latest advancements in model capabilities from ICML'24, EMNLP'24, and NeurIPS'25 as provided by EAGLE-1, EAGLE-2, or EAGLE-3. ### 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 the specific advancements and techniques implemented in ICML'24 papers are not relevant to your project. In cases where speculative decoding does not align with the goals or methods of your application, opting for EAGLE may not be beneficial. ### Is Awesome-LLM-Compression or EAGLE more popular on GitHub? EAGLE has more GitHub stars (2,510 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: Other). ### 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/safeailab-eagle/alternatives) ([Awesome-LLM-Compression markdown twin](/tools/huangowen-awesome-llm-compression/alternatives.md), [EAGLE markdown twin](/tools/safeailab-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-safeailab-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: Slowing. 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/safeailab-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/_