**Nunchaku** is a high-performance inference engine optimized for 4-bit neural networks, as introduced in our paper [SVDQuant](http://arxiv.org/abs/2411.05007). For the underlying quantization library, check out [DeepCompressor](https://github.com/nunchaku-tech/deepcompressor).
-[AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration](https://arxiv.org/abs/2306.00978), MLSys 2024
-[DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models](https://arxiv.org/abs/2402.19481), CVPR 2024
-[QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving](https://arxiv.org/abs/2405.04532), MLSys 2025
-[SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers](https://arxiv.org/abs/2410.10629), ICLR 2025
-[Radial Attention: $O(n \log n)$ Sparse Attention with Energy Decay for Long Video Generation](https://github.com/mit-han-lab/radial-attention), ArXiv 2025
## 引用
如果您觉得 `nunchaku` 对您的研究有用或相关，请引用我们的论文：
```bibtex
@inproceedings{
li2024svdquant,
title={SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models},
author={Li*, Muyang and Lin*, Yujun and Zhang*, Zhekai and Cai, Tianle and Li, Xiuyu and Guo, Junxian and Xie, Enze and Meng, Chenlin and Zhu, Jun-Yan and Han, Song},
booktitle={The Thirteenth International Conference on Learning Representations},
[](https://www.star-history.com/#nunchaku-tech/nunchaku&Date)
