Hunyuan

Install and Run Hunyuan 7B on Windows: A Step-by-Step Guide

Anas Mohammad

Aug 4, 2025 • 4 min read

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.

1. What is Hunyuan 7B?

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.

Key Features

State-of-the-art text generation and comprehension
Multimodal capabilities (text, image, video)
Instruction-following and prompt adaptation

2. System Requirements

Running large models like Hunyuan 7B efficiently requires significant computing resources.

Hardware Requirements

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+

Quantized versions may run on GPUs with 8GB VRAM
For best results, use RTX, A100, or comparable GPUs

Software Requirements

Windows 10/11 (64-bit)
Python 3.11+
CUDA Toolkit 12.0+
PyTorch 2.0+ (with CUDA support)
Git, Visual Studio Build Tools, Miniconda (recommended)

3. Prerequisites: Preparing Your Windows System

3.1 Update Drivers

Install the latest NVIDIA GPU drivers
Ensure Windows is up to date

3.2 Install Python

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

3.3 Install CUDA Toolkit

Get the latest CUDA version (12.0 or above) from NVIDIA’s official site.

3.4 Install PyTorch with CUDA Support

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
python -c "import torch; print(torch.cuda.is_available())"

3.5 Install Essential Tools

Git for Windows
Visual Studio 2022 Build Tools (C++ and Python development workloads)

4. Major Installation Approaches

A. Native Python Installation (Windows)

Straightforward Python + pip setup on Windows.

B. WSL2 (Windows Subsystem for Linux)

Full Linux environment on Windows with better GPU compatibility.

C. ComfyUI (Recommended for Video/Multimodal Tasks)

Visual workflow manager with node-based GUI and model integration.

5. Step-by-Step Windows Installation (Native Python)

5.1 Install Python Libraries

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

5.2 Clone the Hunyuan Repository

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

5.3 Download Model Weights

5.4 Install Additional Dependencies

pip install flash-attn==2.7.4.post1+cu12torch2.6cxx11abiFALSE
pip install numpy opencv-python pillow

6. Step-by-Step Installation Using WSL2

6.1 Enable and Install WSL2

wsl --install

Then install Ubuntu via Microsoft Store.

6.2 Set Up Ubuntu Environment

sudo apt-get update && sudo apt-get upgrade
sudo apt-get install -y python3 python3-pip git build-essential libgl1

6.3 Install Miniconda

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

6.4 Enable CUDA for WSL

Follow NVIDIA’s official WSL CUDA guide.

Check GPU availability:

nvidia-smi

6.5 Clone & Install Hunyuan

Run the same Git and Python installation steps as the native setup, but inside the WSL terminal.

7. Setting Up ComfyUI for Streamlined Workflow

7.1 Download ComfyUI

ComfyUI GitHub Releases
Extract to a folder on your Windows system

7.2 Add Hunyuan Nodes

cd ComfyUI/custom_nodes
git clone https://github.com/kai/ComfyUI-HunyuanVideoWrapper

7.3 Add Model Weights

Place the models in:

ComfyUI/models/diffusion_models/
ComfyUI/models/text_encoders/
ComfyUI/models/vae/

7.4 Install Dependencies

cd ComfyUI
python -m pip install ninja

Use ComfyUI’s interface to resolve missing packages.

7.5 Launch ComfyUI

Double-click run_nvidia_gpu.bat and open the local web interface.

8. Downloading and Configuring Hunyuan 7B Models

Visit Hugging Face model pages for full/quantized versions
Place weights as per your tool (CLI or ComfyUI)
Use lower-bit models for limited VRAM scenarios

9. Running and Testing Hunyuan 7B

Text Inference with Transformers

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]))

Video Inference via ComfyUI

Load Hunyuan video nodes
Build a pipeline with text input, encoder, and video output
Select weights and prompt, then run the graph

10. Optimization Tips for Low VRAM Systems

Use Q4, GGUF, or GGML quantized weights
Reduce video resolution and duration
Run smaller batches

11. Advanced Usage

11.1 Batch Processing

Use Python/WSL scripts to automate multiple inferences.

11.2 API Integration

Deploy with FastAPI or Flask, or expose endpoints via ComfyUI.

11.3 Custom Node Creation

Build new ComfyUI nodes to expand workflows or streamline complex tasks.

12. Troubleshooting and Community Support

Common Issues

CUDA errors: Check driver and toolkit versions
Package failures: Reinstall dependencies from requirements.txt
VRAM crashes: Lower resolution or switch to quantized models

Get Help

GitHub Issues on Tencent’s model repos
Hugging Face model discussions
Discord/Reddit for ComfyUI and AI model enthusiasts

Conclusion

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.