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Hunyuan 7B, a powerful open-source large language and video generation model developed by Tencent, is gaining widespread attention for its advanced capabilities in natural language and multimodal understanding.
Running such a model on Windows can be challenging—especially compared to native Linux environments—but it's entirely feasible with the right setup.
This guide walks you through every step required to install, configure, and effectively run Hunyuan 7B or its video generation counterparts on a Windows system.
Hunyuan 7B is part of Tencent’s suite of large multimodal models. It includes both pre-trained and instruction-tuned versions tailored for natural language processing, video generation, and image synthesis tasks. It serves as the backbone for AI applications in creative, analytical, and productive domains.
Running large models like Hunyuan 7B efficiently requires significant computing resources.
Component | Minimum | Recommended |
---|---|---|
GPU | NVIDIA 24GB VRAM | 40–80GB VRAM (CUDA supported) |
CPU | Multi-core i7/Ryzen 7 | Latest Gen i9/Ryzen 9 |
RAM | 32GB | 64GB+ |
Storage | 50GB free | NVMe SSD, 100GB+ |
Download Python 3.11+ and ensure it's added to the PATH during setup.
python --version
# Output should confirm version 3.11.x or higher
Get the latest CUDA version (12.0 or above) from NVIDIA’s official site.
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
python -c "import torch; print(torch.cuda.is_available())"
Straightforward Python + pip setup on Windows.
Full Linux environment on Windows with better GPU compatibility.
Visual workflow manager with node-based GUI and model integration.
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
pip install git+https://github.com/huggingface/transformers
pip install -r requirements.txt
git clone https://github.com/Tencent-Hunyuan/Tencent-Hunyuan-7B.git
cd Tencent-Hunyuan-7B
For video models:
git clone https://github.com/TencentARC/ARC-Hunyuan-Video-7B.git
cd ARC-Hunyuan-Video-7B
pip install flash-attn==2.7.4.post1+cu12torch2.6cxx11abiFALSE
pip install numpy opencv-python pillow
wsl --install
Then install Ubuntu via Microsoft Store.
sudo apt-get update && sudo apt-get upgrade
sudo apt-get install -y python3 python3-pip git build-essential libgl1
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
Follow NVIDIA’s official WSL CUDA guide.
Check GPU availability:
nvidia-smi
Run the same Git and Python installation steps as the native setup, but inside the WSL terminal.
cd ComfyUI/custom_nodes
git clone https://github.com/kai/ComfyUI-HunyuanVideoWrapper
Place the models in:
ComfyUI/models/diffusion_models/
ComfyUI/models/text_encoders/
ComfyUI/models/vae/
cd ComfyUI
python -m pip install ninja
Use ComfyUI’s interface to resolve missing packages.
Double-click run_nvidia_gpu.bat
and open the local web interface.
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('tencent/Hunyuan-7B-Instruct')
model = AutoModelForCausalLM.from_pretrained('tencent/Hunyuan-7B-Instruct').cuda()
prompt = "Explain quantum computing in simple terms."
inputs = tokenizer(prompt, return_tensors="pt").to('cuda')
output = model.generate(**inputs)
print(tokenizer.decode(output[0]))
Use Python/WSL scripts to automate multiple inferences.
Deploy with FastAPI or Flask, or expose endpoints via ComfyUI.
Build new ComfyUI nodes to expand workflows or streamline complex tasks.
Running Hunyuan 7B on Windows is both practical and increasingly streamlined. Whether you’re using native Python, WSL, or a GUI like ComfyUI, you can leverage powerful text and video AI models locally with the right setup.
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