Run DeepSeek Locally on Ubuntu

Run DeepSeek Locally on Ubuntu to gain complete control over advanced language models without relying on cloud services. Local AI execution is becoming essential for developers, researchers, and privacy-focused users who want predictable performance, data sovereignty, and lower long-term costs.

DeepSeek offers a modern large language model that can be deployed directly on Linux systems, making it ideal for experimentation, offline workflows, and custom AI applications.

This guide walks you through everything you need from environment setup to optimization using clear steps and real-world best practices.

What Is DeepSeek and Why Run It Locally

DeepSeek is an open-weight large language model designed for reasoning, coding assistance, and general text generation. Unlike cloud-hosted AI tools, DeepSeek allows you to run inference directly on your own hardware.

Benefits of running DeepSeek locally:

• Full data privacy and offline access
• No API usage limits or recurring fees
• Faster responses on capable hardware
• Custom fine-tuning potential

Running DeepSeek locally on Linux is especially attractive because Linux offers excellent GPU support, scripting flexibility, and performance tuning options.

System Requirements for Running DeepSeek on Linux

Before you run DeepSeek locally on Linux, ensure your system meets the following requirements:

Minimum Requirements:

• 64-bit Linux distribution (Ubuntu, Fedora, Arch)
• 16 GB RAM
• Modern multi-core CPU
• Python 3.9 or newer

Recommended Setup:

• NVIDIA GPU with CUDA support
• 32 GB RAM or more
• NVMe SSD for model storage

For GPU acceleration, install the latest NVIDIA drivers and CUDA toolkit.
External reference: NVIDIA CUDA Documentation

Preparing Your Linux Environment

Start by updating your system packages:

sudo apt update && sudo apt upgrade -y

Install essential dependencies:

sudo apt install python3 python3-venv python3-pip git -y

Create a virtual environment to keep dependencies isolated:

python3 -m venv deepseek-env
source deepseek-env/bin/activate

This approach keeps your Linux system clean and avoids package conflicts.

Deploy DeepSeek using Ollama

Once Python and Git are installed, you’re ready to install Ollama and manage DeepSeek. Please run the below command to install Ollama:

curl -fsSL https://ollama.com/install.sh | sh
ollama --version

🔗 External Resource :
Ollama Official Documentation – https://ollama.com/docs

Then, start Ollama and configure it to run automatically on boot.

sudo systemctl start ollama
sudo systemctl enable ollama

With Ollama installed, you can now proceed to install DeepSeek.

ollama run deepseek-r1:7b

Depending on your internet speed this command could take some time to execute. What happens when you run it:

• If the model isn’t already on your machine, Ollama pulls it automatically
• Ollama then loads the model into memory
• You’re dropped into an interactive prompt where you can start chatting with the model

In short: it launches the DeepSeek R1 7B model locally using Ollama, no cloud required.

Once done you can interact with Deepseek using CLI or Ctrl + d or /bye to exit. After the model has finished downloading, verify its availability by running:

ollama list

Run DeepSeek locally on Ubuntu in a Web UI

While Ollama lets you interact with DeepSeek from the command line, you may prefer a more user-friendly web interface. To do this, we’ll use Ollama Web UI, a lightweight web-based interface for working with Ollama models.

First, create a virtual environment to isolate your Python dependencies from the system-wide Python installation.

python3 -m venv ~/open-webui-venv
source ~/open-webui-venv/bin/activate

Now that your virtual environment is active, you can install Open WebUI using pip.

pip install open-webui

Once installed, start the server using.

open-webui serve

Open your web browser and go to http://localhost:8080 to access the Ollama Web UI. On the login page it will ask you to create admin user by entering Name, email address and password.

In the Web UI, select the DeepSeek model from the dropdown menu to start interacting. You can ask questions, generate text, or explore any other tasks supported by DeepSeek.

You can use the UI to interact with DeepSeek just like ChatGPT.

Enable Open-WebUI to Start Automatically on Boot

If you want Open-WebUI to launch automatically when your Linux system boots, you can create a systemd service. This ensures the Open-WebUI server runs in the background without manual intervention.

Open a new service file with your preferred editor:

sudo vim /etc/systemd/system/open-webui.service

Then add the following configuration, making sure to replace your_username with your actual Linux username:

[Unit]
Description=Open WebUI Service
After=network.target

[Service]
User=your_username
WorkingDirectory=/home/your_username/open-webui-venv
ExecStart=/home/your_username/open-webui-venv/bin/open-webui serve
Restart=always
Environment="PATH=/home/your_username/open-webui-venv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"

[Install]
WantedBy=multi-user.target

After saving the file, reload systemd so it recognizes the new service:

sudo systemctl daemon-reload

To make the service start automatically on every boot, run:

sudo systemctl enable open-webui.service
sudo systemctl start open-webui.service

Check that Open-WebUI is running correctly:

sudo systemctl status open-webui.service

You should see output indicating that the service is active and running. If there are any errors, double-check the User and WorkingDirectory paths in your service file.

Final Thoughts

Using Ollama is the simplest and most reliable way to run DeepSeek locally on Linux. It removes complexity, handles model management automatically, and delivers a smooth offline AI experience directly from your terminal.

If you’re looking for privacy, speed, and full system control, Ollama + DeepSeek on Linux is a powerful combination worth adopting.