EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency) is a new baseline for fast decoding of Large Language Models (LLMs) with provable performance maintenance. This approach involves extrapolating the second-top-layer contextual feature vectors of LLMs, enabling a significant boost in generation efficiency.
EAGLE is:
certified by the third-party evaluation as the fastest speculative method so far.
provably maintaining the consistency with vanilla decoding in the distribution of generated texts.
trainable (within 1-2 days) and testable on 8x RTX 3090 GPUs. So even the GPU poor can afford it.
combinable with other parallelled techniques such as vLLM, DeepSpeed, Mamba, FlashAttention, quantization, and hardware optimization.
EAGLE-2 uses the confidence scores from the draft model to approximate acceptance rates, dynamically adjusting the draft tree structure, which further enhances performance.
EAGLE-2 is:
4x faster than vanilla decoding (13B).
1.4x faster than EAGLE-1 (13B).
EAGLE-3 removes the feature prediction constraint in EAGLE and simulates this process during training using training-time testing. Considering that top-layer features are limited to next-token prediction, EAGLE-3 replaces them with a fusion of low-, mid-, and high-level semantic features.
EAGLE-3 further improves generation speed while ensuring lossless performance.
EAGLE-3 is:
5.6 faster than vanilla decoding (13B).
1.8x faster than EAGLE-1 (13B).
demogif
Inference is conducted on 2x RTX 3090 GPUs at fp16 precision using the Vicuna 13B model.
Support
EAGLE has been merged in the following mainstream LLM serving frameworks (listed in alphabetical order).
@inproceedings{li2024eagle,
author = {Yuhui Li and Fangyun Wei and Chao Zhang and Hongyang Zhang},
title = {{EAGLE}: Speculative Sampling Requires Rethinking Feature Uncertainty},
booktitle = {International Conference on Machine Learning},
year = {2024}
}
@inproceedings{li2024eagle2,
author = {Yuhui Li and Fangyun Wei and Chao Zhang and Hongyang Zhang},
title = {{EAGLE-2}: Faster Inference of Language Models with Dynamic Draft Trees},
booktitle = {Empirical Methods in Natural Language Processing},
year = {2024}
}
@misc{li2025eagle3scalinginferenceacceleration,
title={{EAGLE-3}: Scaling up Inference Acceleration of Large Language Models via Training-Time Test},
author={Yuhui Li and Fangyun Wei and Chao Zhang and Hongyang Zhang},
year={2025},
eprint={2503.01840},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.01840},
}
