Model reference · open weights
Fun-Audio is an open-weight language model from FunAudioLLM. Fun-Audio-Chat-8B (BF16) weighs 18.9 GB; the smallest configuration that runs it is 2× RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the FunAudioLLM/Fun-Audio-Chat-8B model card, 2026-11-04
What it is
| Released by | FunAudioLLM |
|---|---|
| Released | 2025-12-23 |
| Parameters | 9.5B |
| VRAM | 18.9 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB … RTX 4060 Ti 16 GB 2 smaller cards | ||||
| RTX 3090 24 GB | 3 | 24K | 23.4 GB | |
| RTX 4090 24 GB | 3 | 24K | 23.4 GB | |
| RTX 5090 32 GB | 9 | 2 | 75K | 31.0 GB |
| L40S 48 GB | 20 | 5 | 160K | 44.0 GB |
| A100 80 GB | 48 | 12 | all 256K | 78.2 GB |
| H100 80 GB | 44 | 11 | all 256K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 57 | 14 | all 256K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 69 | 17 | all 256K | 107 GB |
| H200 141 GB | 94 | 23 | all 256K | 138 GB |
| B200 180 GB | 125 | 31 | all 256K | 176 GB |
| 2× RTX 3060 12 GB tensor parallel | 2 | 18K | 11.6 GB a card | |
| 2× RTX 4060 Ti 16 GB tensor parallel | 8 | 2 | 68K | 15.4 GB a card |
| 2× RTX 4090 24 GB tensor parallel | 21 | 5 | 174K | 23.4 GB a card |
| 2× RTX 3090 24 GB tensor parallel | 21 | 5 | 174K | 23.4 GB a card |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 20.9 GB | 24.5 GB |
| 5 | 25.7 GB | 43.8 GB |
| 8 | 29.3 GB | 58.3 GB |
| 16 | 39.0 GB | 97.0 GB |
| 32 | 58.3 GB | 174 GB |
| 64 | 97.0 GB | 329 GB |
One card, with vLLM's small-card settings.
From the model card
Fun-Audio-Chat is a Large Audio Language Model built for natural, low-latency voice interactions.
Fun-Audio-Chat is a Large Audio Language Model built for natural, low-latency voice interactions. It introduces Dual-Resolution Speech Representations (an efficient 5Hz shared backbone + a 25Hz refined head) to cut compute while keeping high speech quality, and Core-Cocktail training to preserve strong text LLM capabilities. It delivers top-tier results on spoken QA, audio understanding, speech function calling, speech instruction-following and voice empathy benchmarks.
| Attribute | Value |
|---|---|
| Model Size | ~8B parameters |
| Architecture | Dual-Resolution Speech Representations |
| Languages | English, Chinese |
| License | Apache 2.0 |
git clone --recurse-submodules https://github.com/FunAudioLLM/Fun-Audio-Chat
cd Fun-Audio-Chat
apt install ffmpeg
conda create -n FunAudioChat python=3.12 -y
conda activate FunAudioChat
pip install torch==2.8.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu128
pip install -r requirements.txt
Using HuggingFace:
pip install huggingface-hub
hf download FunAudioLLM/Fun-Audio-Chat-8B --local-dir ./pretrained_models/Fun-Audio-Chat-8B
hf download FunAudioLLM/Fun-CosyVoice3-0.5B-2512 --local-dir ./pretrained_models/Fun-CosyVoice3-0.5B-2512
Or using ModelScope:
modelscope download --model FunAudioLLM/Fun-Audio-Chat-8B --local_dir pretrained_models/Fun-Audio-Chat-8B
modelscope download --model FunAudioLLM/Fun-CosyVoice3-0.5B-2512 --local_dir pretrained_models/Fun-CosyVoice3-0.5B-2512
export PYTHONPATH=`pwd`
# Speech-to-Text
python examples/infer_s2t.py
# Speech-to-Speech
python examples/infer_s2s.py
| Benchmark | Category |
|---|---|
| OpenAudioBench | Spoken QA |
| VoiceBench | Spoken QA |
| UltraEval-Audio | Speech-to-Speech |
| MMAU, MMAU-Pro, MMSU | Audio Understanding |
| Speech-ACEBench, Speech-BFCL, Speech-SmartInteract | Speech Function Calling |
| VStyle | Speech Instruction-Following |
For detailed evaluation instructions, please refer to the GitHub repository.
If you find this model useful, please cite our paper:
@article{funaudiochat2025,
title={Fun-Audio-Chat Technical Report},
author={Qian Chen and Luyao Cheng and Chong Deng and Xiangang Li and Jiaqing Liu and Chao-Hong Tan and Wen Wang and Junhao Xu and Jieping Ye and Qinglin Zhang and Qiquan Zhang and Jingren Zhou},
year={2025},
eprint={2512.20156},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2512.20156},
}
@misc{tan2025drvoiceparallelspeechtextvoice,
title={DrVoice: Parallel Speech-Text Voice Conversation Model via Dual-Resolution Speech Representations},
author={Chao-Hong Tan and Qian Chen and Wen Wang and Chong Deng and Qinglin Zhang and Luyao Cheng and Hai Yu and Xin Zhang and Xiang Lv and Tianyu Zhao and Chong Zhang and Yukun Ma and Yafeng Chen and Hui Wang and Jiaqing Liu and Xiangang Li and Jieping Ye},
year={2025},
eprint={2506.09349},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.09349},
}
This model is licensed under the Apache 2.0 License.
This project is based on the following excellent open-source projects:
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.