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Comparison

EAGLE vs Awesome-LLM-Inference

Verdict

Pick EAGLE if eAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding; pick Awesome-LLM-Inference if awesome-LLM-Inference is a well-curated list of papers and codes related to efficient inference techniques for large language models and vision-language models, featuring methods like Flash-Attention and Paged-Attention.

Markdown twin · EAGLE alternatives · Awesome-LLM-Inference alternatives

GraphCanon updated 1d

EAGLE logo

EAGLE

SafeAILab/EAGLE

★ 2.5kpushed Feb 20, 2026
vs
Awesome-LLM-Inference logo

Awesome-LLM-Inference

xlite-dev/Awesome-LLM-Inference

★ 5.5kpushed Aug 14, 2026

Trust & integrity

SignalEAGLEAwesome-LLM-Inference
Maintenance
Slowing (155d since push)
As of 1mo · github_public_v1
Active (10d since push)
As of 1d · github_public_v1
Provenance
Not a fork · Organization account
As of 1mo · github_public_v1
Not a fork · Organization account
As of 1d · 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
EAGLE trust reportAwesome-LLM-Inference trust reportTrust methodology

Tagline

EAGLE
Official Implementation of EAGLE Series Models
Awesome-LLM-Inference
A curated list of LLM/VLM inference papers with codes

Stars

EAGLE
2.5k
Awesome-LLM-Inference
5.5k

Forks

EAGLE
297
Awesome-LLM-Inference
429

Open issues

EAGLE
101
Awesome-LLM-Inference
6

Language

EAGLE
Python
Awesome-LLM-Inference
Python

Adopt for

EAGLE
EAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding.
Awesome-LLM-Inference
Awesome-LLM-Inference is a well-curated list of papers and codes related to efficient inference techniques for large language models and vision-language models, featuring methods like Flash-Attention and Paged-Attention.

Persona

EAGLE
-
Awesome-LLM-Inference
-

Runtime

EAGLE
-
Awesome-LLM-Inference
-

License

EAGLE
Other
Awesome-LLM-Inference
The tool is licensed under GPL-3.0, which may affect how it can be integrated into other projects depending on their licensing needs.

Last pushed

EAGLE
Feb 20, 2026
Awesome-LLM-Inference
Aug 14, 2026

Categories

EAGLE
Inference & Serving, LLM Frameworks
Awesome-LLM-Inference
Inference & Serving

Trust and health

Maintenance

EAGLE
Slowing (36%)
Awesome-LLM-Inference
Active (82%)

Days since push

EAGLE
155d
Awesome-LLM-Inference
10d

Open issues (now)

EAGLE
101
Awesome-LLM-Inference
6

Stars delta

EAGLE
Unknown
Awesome-LLM-Inference
+62 (30d)

Open issues delta

EAGLE
Unknown
Awesome-LLM-Inference
0 (30d)

Full report

EAGLE
Trust report
Awesome-LLM-Inference
Trust report

Choose EAGLE if…

  • License: EAGLE is Other, Awesome-LLM-Inference is GPL-3.0.
  • Tags unique to EAGLE: large language models, llm-inference, speculative-decoding.
  • Also covers LLM Frameworks.
  • 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.
GitHubDetails

Choose Awesome-LLM-Inference if…

  • License: Awesome-LLM-Inference is GPL-3.0, EAGLE is Other.
  • Requirements: Requires Python for the use of included codes and to understand the methods described in the associated papers..
  • Tags unique to Awesome-LLM-Inference: flash-attention, paged-attention, parallelism, wint8/4.
  • Use Awesome-LLM-Inference when you are looking to optimize the performance of your large language model or vision-language model inference with cutting-edge techniques such as Flash-Attention.

When NOT to use Awesome-LLM-Inference

  • Do not use Awesome-LLM-Inference if your project strictly conforms to licenses different from GPL-3.0, as its licensing could be incompatible with your project's license requirements.
  • Avoid using this tool for immediate production implementation of inference techniques without additional vetting since the repository itself may contain unvetted research papers and code snippets.
GitHubDetails

Explore

EAGLE trust report →Awesome-LLM-Inference trust report →Inference & Serving category →LLM Frameworks category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: EAGLE 2.5k · Awesome-LLM-Inference 5.5k (synced Aug 24, 2026).

Common questions

What is the difference between EAGLE and Awesome-LLM-Inference?
EAGLE: Official Implementation of EAGLE Series Models. Awesome-LLM-Inference: A curated list of LLM/VLM inference papers with codes. See the comparison table for live GitHub stats and shared categories.
When should I choose EAGLE over Awesome-LLM-Inference?
Choose EAGLE over Awesome-LLM-Inference when License: EAGLE is Other, Awesome-LLM-Inference is GPL-3.0; Tags unique to EAGLE: large language models, llm-inference, speculative-decoding; Also covers LLM Frameworks; 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 choose Awesome-LLM-Inference over EAGLE?
Choose Awesome-LLM-Inference over EAGLE when License: Awesome-LLM-Inference is GPL-3.0, EAGLE is Other; Requirements: Requires Python for the use of included codes and to understand the methods described in the associated papers.; Tags unique to Awesome-LLM-Inference: flash-attention, paged-attention, parallelism, wint8/4; Use Awesome-LLM-Inference when you are looking to optimize the performance of your large language model or vision-language model inference with cutting-edge techniques such as Flash-Attention.
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.
When should I avoid Awesome-LLM-Inference?
Do not use Awesome-LLM-Inference if your project strictly conforms to licenses different from GPL-3.0, as its licensing could be incompatible with your project's license requirements. Avoid using this tool for immediate production implementation of inference techniques without additional vetting since the repository itself may contain unvetted research papers and code snippets.
Is EAGLE or Awesome-LLM-Inference more popular on GitHub?
Awesome-LLM-Inference has more GitHub stars (5,477 vs 2,510). Stars measure visibility, not whether either tool fits your constraints.
Are EAGLE and Awesome-LLM-Inference open source?
Yes - both are open-source projects on GitHub (EAGLE: Other, Awesome-LLM-Inference: GPL-3.0).
Where can I find alternatives to EAGLE or Awesome-LLM-Inference?
GraphCanon lists graph-backed alternatives at EAGLE alternatives and Awesome-LLM-Inference alternatives (EAGLE markdown twin, Awesome-LLM-Inference 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, EAGLE or Awesome-LLM-Inference?
EAGLE: Slowing. Awesome-LLM-Inference: 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 EAGLE and Awesome-LLM-Inference?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: EAGLE trust report; Awesome-LLM-Inference trust report.

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