--- title: "Eagle vs EAGLE" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvlabs-eagle-vs-safeailab-eagle" tools: ["nvlabs-eagle", "safeailab-eagle"] --- # Eagle vs EAGLE *GraphCanon updated Aug 24, 2026* ## Verdict Pick Eagle if eagle: Frontier Vision-Language Models with Data-Centric Strategies; pick EAGLE if eAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding. [Eagle](https://nvlabs.github.io/Eagle/) reports 3.4k GitHub stars, 327 forks, and 62 open issues, last pushed Jun 24, 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 [Eagle's repository](https://github.com/NVlabs/Eagle) and [EAGLE's repository](https://github.com/SafeAILab/EAGLE). | | [Eagle](/tools/nvlabs-eagle.md) | [EAGLE](/tools/safeailab-eagle.md) | | --- | --- | --- | | Tagline | Frontier Vision-Language Models with Data-Centric Strategies | Official Implementation of EAGLE Series Models | | Stars | 3,407 | 2,510 | | Forks | 327 | 297 | | Open issues | 62 | 101 | | Language | Python | Python | | Adopt for | Eagle: Frontier Vision-Language Models with Data-Centric Strategies | EAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding. | | Persona | - | - | | Runtime | - | - | | License | The code is released under Apache 2.0 license, while the pretrained models are under CC BY-NC 4.0 or NVIDIA licenses for non-commercial use only. | Other | | Categories | Computer Vision, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Eagle](/tools/nvlabs-eagle.md) | [EAGLE](/tools/safeailab-eagle.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 54d | 155d | | Open issues (now) | 62 | 101 | | Stars delta | +199 (30d) | Unknown | | Open issues delta | +3 (30d) | Unknown | | Full report | [trust report](/tools/nvlabs-eagle/trust.md) | [trust report](/tools/safeailab-eagle/trust.md) | ## Decision facts: Eagle - **Pricing:** freemium - Free for non-commercial use; requires adherence to licensing agreements - **Requirements:** Min 8 GB RAM; Ensure compliance with all applicable laws and regulations when using the dataset and model weights. - **Adopt for:** Eagle: Frontier Vision-Language Models with Data-Centric Strategies - **License detail:** The code is released under Apache 2.0 license, while the pretrained models are under CC BY-NC 4.0 or NVIDIA licenses for non-commercial use only. ## 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 Eagle if… - License: Eagle is Apache-2.0, EAGLE is Other. - Pricing: Free for non-commercial use; requires adherence to licensing agreements. - Requirements: Min 8 GB RAM; Ensure compliance with all applicable laws and regulations when using the dataset and model weights.. - Tags unique to Eagle: data-centric-strategies, gpt4, huggingface, llm-improvements. - Also covers Computer Vision. - When you need advanced vision-language models enhanced by data-centric strategies developed by NVlabs and improved using Qwen. ### Choose EAGLE if… - License: EAGLE is Other, Eagle is Apache-2.0. - Tags unique to EAGLE: large language models, llm-inference, speculative-decoding. - Also covers Inference & Serving. - 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 your project requires commercial use, as Eagle's models are intended for non-commercial use only under the CC BY-NC 4.0 License or NVIDIA License. - In situations where you require a vision-language model that does not rely on improvements made using Qwen. ## 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 Eagle and EAGLE? Eagle: Frontier Vision-Language Models with Data-Centric Strategies. EAGLE: Official Implementation of EAGLE Series Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose Eagle over EAGLE? Choose Eagle over EAGLE when License: Eagle is Apache-2.0, EAGLE is Other; Pricing: Free for non-commercial use; requires adherence to licensing agreements; Requirements: Min 8 GB RAM; Ensure compliance with all applicable laws and regulations when using the dataset and model weights.; Tags unique to Eagle: data-centric-strategies, gpt4, huggingface, llm-improvements; Also covers Computer Vision; When you need advanced vision-language models enhanced by data-centric strategies developed by NVlabs and improved using Qwen. ### When should I choose EAGLE over Eagle? Choose EAGLE over Eagle when License: EAGLE is Other, Eagle is Apache-2.0; Tags unique to EAGLE: large language models, llm-inference, speculative-decoding; Also covers Inference & Serving; 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 Eagle? If your project requires commercial use, as Eagle's models are intended for non-commercial use only under the CC BY-NC 4.0 License or NVIDIA License. In situations where you require a vision-language model that does not rely on improvements made using Qwen. ### 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 Eagle or EAGLE more popular on GitHub? Eagle has more GitHub stars (3,407 vs 2,510). Stars measure visibility, not whether either tool fits your constraints. ### Are Eagle and EAGLE open source? Yes - both are open-source projects on GitHub (Eagle: Apache-2.0, EAGLE: Other). ### Where can I find alternatives to Eagle or EAGLE? GraphCanon lists graph-backed alternatives at [Eagle alternatives](/tools/nvlabs-eagle/alternatives) and [EAGLE alternatives](/tools/safeailab-eagle/alternatives) ([Eagle markdown twin](/tools/nvlabs-eagle/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/nvlabs-eagle-vs-safeailab-eagle.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Eagle or EAGLE? Eagle: 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 Eagle and EAGLE? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Eagle trust report](/tools/nvlabs-eagle/trust); [EAGLE trust report](/tools/safeailab-eagle/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvlabs-eagle`](/api/graphcanon/graph?tool=nvlabs-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/_