--- title: "EAGLE vs Awesome-LLM-Inference" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/safeailab-eagle-vs-xlite-dev-awesome-llm-inference" tools: ["safeailab-eagle", "xlite-dev-awesome-llm-inference"] --- # EAGLE vs Awesome-LLM-Inference *GraphCanon updated Aug 24, 2026* ## 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. [EAGLE](https://arxiv.org/pdf/2503.01840) reports 2.5k GitHub stars, 297 forks, and 101 open issues, last pushed Feb 20, 2026. [Awesome-LLM-Inference](https://github.com/xlite-dev/Awesome-LLM-Inference) has 5.5k stars, 429 forks, and 6 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [EAGLE's repository](https://github.com/SafeAILab/EAGLE) and [Awesome-LLM-Inference's repository](https://github.com/xlite-dev/Awesome-LLM-Inference). | | [EAGLE](/tools/safeailab-eagle.md) | [Awesome-LLM-Inference](/tools/xlite-dev-awesome-llm-inference.md) | | --- | --- | --- | | Tagline | Official Implementation of EAGLE Series Models | A curated list of LLM/VLM inference papers with codes | | Stars | 2,510 | 5,477 | | Forks | 297 | 429 | | Open issues | 101 | 6 | | Language | Python | Python | | Adopt for | EAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding. | 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 | - | - | | Runtime | - | - | | License | Other | The tool is licensed under GPL-3.0, which may affect how it can be integrated into other projects depending on their licensing needs. | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [EAGLE](/tools/safeailab-eagle.md) | [Awesome-LLM-Inference](/tools/xlite-dev-awesome-llm-inference.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 155d | 10d | | Open issues (now) | 101 | 6 | | Stars delta | Unknown | +62 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/safeailab-eagle/trust.md) | [trust report](/tools/xlite-dev-awesome-llm-inference/trust.md) | ## Decision facts: EAGLE - **Adopt for:** EAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding. ## Decision facts: Awesome-LLM-Inference - **Requirements:** Requires Python for the use of included codes and to understand the methods described in the associated papers. - **Adopt for:** 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. - **License detail:** The tool is licensed under GPL-3.0, which may affect how it can be integrated into other projects depending on their licensing needs. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/safeailab-eagle/alternatives) and [Awesome-LLM-Inference alternatives](/tools/xlite-dev-awesome-llm-inference/alternatives) ([EAGLE markdown twin](/tools/safeailab-eagle/alternatives.md), [Awesome-LLM-Inference markdown twin](/tools/xlite-dev-awesome-llm-inference/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/safeailab-eagle-vs-xlite-dev-awesome-llm-inference.md) 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](/tools/safeailab-eagle/trust); [Awesome-LLM-Inference trust report](/tools/xlite-dev-awesome-llm-inference/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=safeailab-eagle`](/api/graphcanon/graph?tool=safeailab-eagle) - 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/_