Running DeepSeek Prover V2 7B on Windows involves several key steps—ranging from environment preparation to downloading and executing the model. This guide walks you through everything you need to install and run DeepSeek Prover V2 7B on a Windows system effectively.

What Is DeepSeek Prover V2 7B?

DeepSeek Prover V2 is an advanced large language model built for formal mathematical reasoning using Lean 4. It excels at recursive problem-solving, breaking down complex math problems into smaller steps and generating verifiable formal proofs.

The 7B parameter version strikes a balance between power and accessibility. Unlike its larger 671B counterpart, it can be deployed locally or on cloud systems with high-end GPUs, making it ideal for researchers and developers with limited hardware resources.

Windows Hardware & System Requirements

To run DeepSeek Prover V2 7B locally on Windows, you’ll need a strong hardware setup:

• GPU: NVIDIA RTX A6000 or equivalent (≥ 32GB VRAM)
• Storage: 100GB free disk space
• RAM: 64GB system memory
• CPU: Intel i7/i9 or AMD Ryzen 7/9
• OS: Windows 10 or 11 with WSL2 (Windows Subsystem for Linux) enabled

Step-by-Step Installation Guide

1. Enable WSL2 and Set Up Ubuntu

Open PowerShell as Administrator and run:

wsl --install

After rebooting, open the Microsoft Store, install Ubuntu, and complete the setup inside the terminal.

2. Install NVIDIA Drivers and CUDA Toolkit

• Install the latest NVIDIA drivers compatible with your GPU.
• Download and install the CUDA Toolkit version matching your target PyTorch build.

3. Install Anaconda

Download Anaconda for Windows and install it to simplify Python environment management.

Python Environment and Dependencies

1. Create Python Environment

Launch WSL Ubuntu terminal or the Anaconda Prompt:

conda create -n deepseek python=3.11
conda activate deepseek

2. Install Required Packages

Inside the activated environment:

pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117
pip install "transformers>=4.38.0" "accelerate>=0.25.0" "bitsandbytes>=0.41.0" einops
conda install -c conda-forge notebook ipywidgets -y

• Use the appropriate CUDA version (e.g., cu118) if your system differs.
• bitsandbytes is required for 8-bit quantization efficiency.
• notebook and ipywidgets enable interactive exploration with Jupyter.

Downloading the DeepSeek Prover V2 7B Model

Use the Hugging Face transformers library to load the model:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "deepseek-ai/DeepSeek-Prover-V2-7B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
)

• device_map="auto" enables automatic GPU/CPU assignment.
• The model and tokenizer will download from Hugging Face on first run.

Running the Model Locally

To run the model, use a simple Python script or a Jupyter Notebook:

input_text = "Prove that the sum of two even numbers is even."
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

This generates a formal proof or reasoning chain based on the input prompt.

Optional: Use NodeShift for Cloud Deployment

If your local GPU isn’t sufficient, you can run DeepSeek Prover V2 in the cloud using services like NodeShift:

1. Create an account at NodeShift.
2. Launch a GPU instance (A6000 or higher).
3. SSH into the server and repeat the setup steps.
4. Use SSH port forwarding to access Jupyter remotely via your browser.

Performance Optimization Tips

• Use mixed precision (torch_dtype=torch.bfloat16) to save VRAM.
• Always run the model in an isolated virtual environment.
• Keep GPU drivers and CUDA versions updated.
• Use interactive notebooks for real-time debugging and testing.

Troubleshooting Guide

Out of Memory (OOM) Errors

• Lower the batch size or sequence length.
• Enable mixed precision or use a cloud provider.

Package Conflicts

• Use a clean conda environment and install dependencies in small steps.

CUDA Not Detected

• Run torch.cuda.is_available() to verify.
• Reinstall NVIDIA drivers and CUDA toolkit if needed.

Jupyter Issues

Use correct SSH port forwarding:

ssh -L 8888:localhost:8888 user@remote-host

Conclusion

Installing and running DeepSeek Prover V2 7B on Windows is very achievable if you follow a structured setup using WSL2, Anaconda, and modern GPU hardware. Whether you’re building a local reasoning engine or testing mathematical proofs in the cloud, this guide gives you all the steps to get started quickly and effectively.

References

1. Run DeepSeek Janus-Pro 7B on Mac: A Comprehensive Guide Using ComfyUI
2. Run DeepSeek Janus-Pro 7B on Mac: Step-by-Step Guide
3. Run DeepSeek Janus-Pro 7B on Windows: A Complete Installation Guide
4. Running DeepSeek Prover V2 7B on macOS: A Comprehensive Guide