---

license: apache-2.0 (Commercial applications are also allowed!)

SageAttention3

This repository provides the official implementation of SageAttention3

SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training
Paper: https://arxiv.org/abs/2505.11594
Jintao Zhang, Jia Wei, Pengle Zhang, Xiaoming Xu, Haofeng Huang, Haoxu Wang, Kai Jiang, Jun Zhu, Jianfei Chen

Limitaitions:

Currently, SageAttention3 works well for:

1. Video generation models: CogVideoX-2B, HunyuanVideo, Mochi.
2. Almost all image generation models, including Flux and Stable-Diffusion3.5.

Note: SageAttention3 does not guarantee lossless acceleration for all models. For other video generation models, we recommend selectively using SageAttention2++ in certain layers or timesteps.

For example:

• Apply SageAttention2++ only at the first and last timesteps,
• Use SageAttention3 for all the others.

This hybrid approach may achieve lossless acceleration.

Installation

Base environment

• python>=3.13 , torch>=2.8.0, CUDA >=12.8

Install Package

To use SageAttention3, please compile from source:

git clone https://huggingface.co/jt-zhang/SageAttention3
cd SageAttention3 
python setup.py install

How to Use

from sageattn import sageattn_blackwell
attn_output = sageattn_blackwell(q, k, v, is_causal=False)

• q, k, v are FP16/BF16 dtype with the shape (batch_size, head_num, seq_len, head_dim)
• is_causal determines the use of a causal mask.

Performance

Speed of Kernels

Video and Image Generation Examples

Citation

If you use this code or find our work valuable, please cite:

@inproceedings{zhang2025sageattention,
  title={SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration}, 
  author={Zhang, Jintao and Wei, Jia and Zhang, Pengle and Zhu, Jun and Chen, Jianfei},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2025}
}
@inproceedings{zhang2024sageattention2,
  title={Sageattention2: Efficient attention with thorough outlier smoothing and per-thread int4 quantization},
  author={Zhang, Jintao and Huang, Haofeng and Zhang, Pengle and Wei, Jia and Zhu, Jun and Chen, Jianfei},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2025}
}
@article{zhang2025sageattention2++,
  title={Sageattention2++: A more efficient implementation of sageattention2},
  author={Zhang, Jintao and Xu, Xiaoming and Wei, Jia and Huang, Haofeng and Zhang, Pengle and Xiang, Chendong and Zhu, Jun and Chen, Jianfei},
  journal={arXiv preprint arXiv:2505.21136},
  year={2025}
}
@article{zhang2025sageattention3,
  title={SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training},
  author={Zhang, Jintao and Wei, Jia and Zhang, Pengle and Xu, Xiaoming and Huang, Haofeng and Wang, Haoxu and Jiang, Kai and Zhu, Jun and Chen, Jianfei},
  journal={arXiv preprint arXiv:2505.11594},
  year={2025}
}