Model reference · open weights
Step-Audio-2-mini is an open-weight language model from stepfun-ai. Step-Audio-2-mini (BF16) weighs 16.6 GB; the smallest configuration that runs it is 2× RTX 3060 12 GB.
Summary of the stepfun-ai/Step-Audio-2-mini model card, 2026-10-01
What it is
| Released by | stepfun-ai |
|---|---|
| Released | 2025-08-28 |
| Parameters | 8.3B |
| VRAM | 16.6 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory |
|---|---|---|---|
| 8K each | |||
| RTX 3060 12 GB … RTX 4060 Ti 16 GB 2 smaller cards | — | — | |
| RTX 3090 24 GB | 12 | all 16K | 23.4 GB |
| RTX 4090 24 GB | 12 | all 16K | 23.4 GB |
| RTX 5090 32 GB | 28 | all 16K | 31.0 GB |
| L40S 48 GB | 56 | all 16K | 44.0 GB |
| A100 80 GB | 129 | all 16K | 78.2 GB |
| H100 80 GB | 119 | all 16K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 152 | all 16K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 181 | all 16K | 107 GB |
| H200 141 GB | 246 | all 16K | 138 GB |
| B200 180 GB | 326 | all 16K | 176 GB |
| 2× RTX 3060 12 GB tensor parallel | 10 | all 16K | 11.6 GB a card |
| 2× RTX 4060 Ti 16 GB tensor parallel | 26 | all 16K | 15.4 GB a card |
| Requests at once | 8K tokens each |
|---|---|
| 1 | 17.9 GB |
| 5 | 19.8 GB |
| 8 | 21.2 GB |
| 16 | 25.0 GB |
| 32 | 32.5 GB |
| 64 | 47.5 GB |
One card, with vLLM's small-card settings.
From the model card
Step-Audio 2 is an end-to-end multi-modal large language model designed for industry-strength audio understanding and speech conversation, presented in the paper Step-Audio 2 Technical Report.
Advanced Speech and Audio Understanding: Promising performance in ASR and audio understanding by comprehending and reasoning semantic information, para-linguistic and non-vocal information.
Intelligent Speech Conversation: Achieving natural and intelligent interactions that are contextually appropriate for various conversational scenarios and paralinguistic information.
Tool Calling and Multimodal RAG: By leveraging tool calling and RAG to access real-world knowledge (both textual and acoustic), Step-Audio 2 can generate responses with fewer hallucinations for diverse scenarios, while also having the ability to switch timbres based on retrieved speech.
State-of-the-Art Performance: Achieving state-of-the-art performance on various audio understanding and conversational benchmarks compared to other open-source and commercial solutions. (See Evaluation and Technical Report).
| Models | 🤗 Hugging Face |
|---|---|
| Step-Audio 2 mini | stepfun-ai/Step-Audio-2-mini |
| Step-Audio 2 mini Base | stepfun-ai/Step-Audio-2-mini-Base |
conda create -n stepaudio2 python=3.10
conda activate stepaudio2
pip install transformers==4.49.0 torchaudio librosa onnxruntime s3tokenizer diffusers hyperpyyaml
git clone https://github.com/stepfun-ai/Step-Audio2.git
cd Step-Audio2
git lfs install
git clone https://huggingface.co/stepfun-ai/Step-Audio-2-mini
python examples.py
pip install gradio
python web_demo.py
You can scan the following QR code to join our WeChat group for communication and discussion.
CER for Chinese, Cantonese and Japanese and WER for Arabian and English. N/A indicates that the language is not supported.
StepEval-Audio-Paralinguistic
MMAU
StepEval-Audio-Toolcall. Date and time tools have no parameter.
URO-Bench. U. R. O. stands for understanding, reasoning, and oral conversation, respectively.
The model and code in the repository is licensed under Apache 2.0 License.
@misc{wu2025stepaudio2technicalreport,
title={Step-Audio 2 Technical Report},
author={Boyong Wu and Chao Yan and Chen Hu and Cheng Yi and Chengli Feng and Fei Tian and Feiyu Shen and Gang Yu and Haoyang Zhang and Jingbei Li and Mingrui Chen and Peng Liu and Wang You and Xiangyu Tony Zhang and Xingyuan Li and Xuerui Yang and Yayue Deng and Yechang Huang and Yuxin Li and Yuxin Zhang and Zhao You and Brian Li and Changyi Wan and Hanpeng Hu and Jiangjie Zhen and Siyu Chen and Song Yuan and Xuelin Zhang and Yimin Jiang and Yu Zhou and Yuxiang Yang and Bingxin Li and Buyun Ma and Changhe Song and Dongqing Pang and Guoqiang Hu and Haiyang Sun and Kang An and Na Wang and Shuli Gao and Wei Ji and Wen Li and Wen Sun and Xuan Wen and Yong Ren and Yuankai Ma and Yufan Lu and Bin Wang and Bo Li and Changxin Miao and Che Liu and Chen Xu and Dapeng Shi and Dingyuan Hu and Donghang Wu and Enle Liu and Guanzhe Huang and Gulin Yan and Han Zhang and Hao Nie and Haonan Jia and Hongyu Zhou and Jianjian Sun and Jiaoren Wu and Jie Wu and Jie Yang and Jin Yang and Junzhe Lin and Kaixiang Li and Lei Yang and Liying Shi and Li Zhou and Longlong Gu and Ming Li and Mingliang Li and Mingxiao Li and Nan Wu and Qi Han and Qinyuan Tan and Shaoliang Pang and Shengjie Fan and Siqi Liu and Tiancheng Cao and Wanying Lu and Wenqing He and Wuxun Xie and Xu Zhao and Xueqi Li and Yanbo Yu and Yang Yang and Yi Liu and Yifan Lu and Yilei Wang and Yuanhao Ding and Yuanwei Liang and Yuanwei Lu and Yuchu Luo and Yuhe Yin and Yumeng Zhan and Yuxiang Zhang and Zidong Yang and Zixin Zhang and Binxing Jiao and Daxin Jiang and Heung-Yeung Shum and Jiansheng Chen and Jing Li and Xiangyu Zhang and Yibo Zhu},
year={2025},
eprint={2507.16632},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2507.16632},
}
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.