Boost Decoding Efficiency via High-Locality Offloading
Overview
NOSA is a trainable sparse attention mechanism designed for KV-cache offloading with an explicit locality constraint, paired with an inference system (NOSI) to realize its efficiency. It improves long-context/long-generation quality over prior offloading baselines while boosting decoding throughput by up to 5.04× vs FullAttn, 1.92× vs InfLLMv2, and 1.83× vs ShadowKV on 1B/3B/8B LLMs.
This repository contains the weights for the NOSA-3B model. For more details, please refer to the paper: NOSA: Native and Offloadable Sparse Attention.
Models
We train 1B, 3B, and 8B models (FullAttn, InfLLMv2, DMA, and NOSA), resulting in a total of 12 models. The following models have been released on Hugging Face:
| Model | Link |
|---|---|
| NOSA-1B | NOSA-1B |
| NOSA-3B | NOSA-3B |
| NOSA-8B | NOSA-8B |
Please reach out to us if additional baseline models (FullAttn, InfLLMv2, or DMA) are needed. You may open an issue or contact us directly via email (our email addresses are provided in the paper).
Citation
@article{huang2025nosa,
title={NOSA: Native and Offloadable Sparse Attention},
author={Huang, Yuxiang and Wang, Pengjie and Han, Jicheng and Zhao, Weilin and Su, Zhou and Sun, Ao and Lyu, Hongya and Zhao, Hengyu and Wang, Yudong and Xiao, Chaojun and Han, Xu and Liu, Zhiyuan},
journal={arXiv preprint arXiv:2510.13602},
year={2025}
}