Run DeepSeek Locally: A Comprehensive Guide to Local LLM Deployment

Are you experiencing slow response times or system overload when using DeepSeek? Deploying DeepSeek Large Language Models (LLMs) locally can significantly improve performance and provide more control over your AI interactions. This guide walks you through the process of setting up DeepSeek on your local machine using Ollama, a powerful open-source framework designed to simplify local LLM deployment.

Why Deploy DeepSeek Locally?

• Improved Performance: Local deployment eliminates network latency, resulting in faster response times.
• Enhanced Privacy: Your data stays on your machine, ensuring greater privacy and security.
• Customization: You have full control over the model and its configuration.
• Offline Access: Use DeepSeek even without an internet connection.

Installing Ollama: Your Gateway to Local LLMs

Ollama is an open-source tool that makes it incredibly easy to download, install, and run LLMs locally. Here's how to get started:

1. Download Ollama: Visit the Ollama website and download the appropriate installer for your operating system.
2. Install and Launch: Follow the installation instructions for your system. Once installed, launch Ollama.

Essential Ollama Commands

Ollama provides a set of simple commands for managing your LLMs:

• ollama run <model_name>: Runs the specified model in the command line.
• ollama list: Lists all available models.
• ollama ps: Shows the status of running models.
• ollama rm <model_name>: Removes a model.
• ollama serve: Starts the Ollama API service.

Deploying DeepSeek R1: Step-by-Step

Before deploying DeepSeek R1, ensure your system meets the necessary requirements. A machine with at least 32GB of RAM is recommended, especially for larger models.

1. Find the DeepSeek R1 Model: Go to the DeepSeek R1 on Ollama's library.
2. Copy the Installation Command: Copy the command provided on the page for your desired model size (e.g., ollama run deepseek-r1:8b for the 8B model).
3. Run the Command: Open your terminal and paste the copied command. Ollama will automatically download and set up the DeepSeek R1 model.

Troubleshooting Slow Download Speeds

If you experience slow download speeds during the model installation, try these steps:

• Interrupt and Restart: Use Ctrl + C to cancel the download and then rerun the command.
• Check Network Connection: Ensure you have a stable and fast internet connection.

Integrating DeepSeek with a WebUI

To interact with DeepSeek through a user-friendly interface, you can integrate it with a WebUI. Here are several options:

Option 1: Self-Hosting with Open WebUI and Docker

1. Install Docker: Download and install Docker Desktop.

2. Run Open WebUI: Use the following Docker command to run Open WebUI:

   docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
   

3. Access Open WebUI: Open your web browser and navigate to http://localhost:3000.

   Open WebUI on GitHub

Option 2: Using a Browser Extension

1. Install Page Assist: Install the "Page Assist - 本地 AI 模型的 Web UI" browser extension.
2. Configure the Extension: Access the extension's options page at chrome-extension://jfgfiigpkhlkbnfnbobbkinehhfdhndo/options.html. Page Assist on GitHub

Option 3: Utilizing Third-Party WebUIs (e.g., Chatbox)

1. Set Environment Variables: To allow access from other websites, you need to set the OLLAMA_ORIGINS environment variable.

2. Open Terminal: Open your terminal.

3. Set Variable (MacOS/Zsh Example):

   echo 'export OLLAMA_ORIGINS="*"' >> $HOME/.zshrc
   

4. Restart Ollama: Close and restart the Ollama application.

5. Use Chatbox:

   • Go to Chatbox.
   • Open Settings.
   • Set the model provider to "OLLAMA API."
   • Save your settings.

Conclusion

Deploying DeepSeek locally with Ollama offers a powerful and convenient way to harness the capabilities of large language models on your own machine. By following this guide, you can enjoy improved performance, enhanced privacy, and greater control over your AI interactions.