Comparison
Awesome-LLM-Compression vs EAGLE
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.
Markdown twin · Awesome-LLM-Compression alternatives · EAGLE alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | Awesome-LLM-Compression | EAGLE |
|---|---|---|
| Maintenance | Steady (37d since push) As of 2w · github_public_v1 | Slowing (155d since push) As of 4w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 4w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Awesome-LLM-Compression
- Awesome LLM compression research papers and tools to accelerate LLM training and inference.
- EAGLE
- Official Implementation of EAGLE Series Models
Stars
- Awesome-LLM-Compression
- 1.9k
- EAGLE
- 2.5k
Forks
- Awesome-LLM-Compression
- 129
- EAGLE
- 291
Open issues
- Awesome-LLM-Compression
- 1
- EAGLE
- 101
Language
- Awesome-LLM-Compression
- -
- EAGLE
- Python
Adopt for
- Awesome-LLM-Compression
- 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
- EAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding.
Persona
- Awesome-LLM-Compression
- -
- EAGLE
- -
Runtime
- Awesome-LLM-Compression
- -
- EAGLE
- -
License
- Awesome-LLM-Compression
- MIT License
- EAGLE
- Other
Last pushed
- Awesome-LLM-Compression
- Jun 30, 2026
- EAGLE
- Feb 20, 2026
Categories
- Awesome-LLM-Compression
- Inference & Serving, LLM Frameworks
- EAGLE
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- Awesome-LLM-Compression
- Steady (60%)
- EAGLE
- Slowing (36%)
Days since push
- Awesome-LLM-Compression
- 37d
- EAGLE
- 155d
Open issues (now)
- Awesome-LLM-Compression
- 1
- EAGLE
- 101
Owner type
- Awesome-LLM-Compression
- User
- EAGLE
- Organization
Full report
- Awesome-LLM-Compression
- Trust report
- EAGLE
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (HuangOwen/Awesome-LLM-Compression) · observed Aug 6, 2026
- GitHub forks (HuangOwen/Awesome-LLM-Compression) · observed Aug 6, 2026
- Last push (HuangOwen/Awesome-LLM-Compression) · observed Jun 30, 2026
- License file (MIT) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (SafeAILab/EAGLE) · observed Jul 25, 2026
- GitHub forks (SafeAILab/EAGLE) · observed Jul 25, 2026
- Last push (SafeAILab/EAGLE) · observed Feb 20, 2026
- License file (Other) · observed Jul 25, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLM-Compression 1.9k · EAGLE 2.5k (synced Aug 6, 2026).
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 (2,478 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 and EAGLE alternatives (Awesome-LLM-Compression markdown twin, EAGLE markdown twin), 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 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; EAGLE trust report.