Home/Compare/Awesome-LLM-Compression vs EAGLE

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

Awesome-LLM-Compression logo

Awesome-LLM-Compression

HuangOwen/Awesome-LLM-Compression

1.9kpushed Jun 30, 2026
vs
EAGLE logo

EAGLE

SafeAILab/EAGLE

2.5kpushed Feb 20, 2026

Trust & integrity

SignalAwesome-LLM-CompressionEAGLE
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

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 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.

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