How to Run DeepSeek on Your Own PC Using Ollama (No Cloud, No Cost)

If you've been following the AI space lately, you know that the landscape has been completely disrupted. For years, OpenAI and Anthropic held the crown. Then, a relatively unknown AI lab called DeepSeek released their new models, and the benchmark charts practically broke.

DeepSeek-Coder and their flagship DeepSeek V2 models are outperforming GPT-4 on massive coding and logic benchmarks. But the best part isn't just that they are smart; the best part is that DeepSeek open-sourced the model weights.

This means you don't have to pay an API fee to use the smartest coding model in the world. You can run it right on your desk. Today, I'm going to show you exactly how to download and run DeepSeek on your own hardware using Ollama, completely free.

Running raw AI models used to require writing custom Python scripts, managing PyTorch dependencies, and spending hours dealing with CUDA driver errors. It was a nightmare.

Ollama changed all of that. Ollama is essentially "Docker for LLMs". It wraps the complex model files into a simple package. You just type one command, and Ollama handles the quantization, the RAM allocation, and the GPU acceleration automatically.

If your machine has an Nvidia GPU or an Apple Silicon chip, Ollama will automatically detect it and offload the processing to the GPU for blazing-fast generation speeds.

First things first, we need to install the engine.

• Mac & Windows: Head over to ollama.com/download and grab the native installer. Run it like any normal app.
• Linux: Open your terminal and run the official install script:

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

To verify it's working, open your terminal and type ollama -v. You should see the version number print out.

DeepSeek offers several sizes of their models. The model you choose depends entirely on your computer's RAM.

For most developers with a standard 16GB MacBook or PC, the 6.7B model or the newer V2-Lite is the perfect balance of speed and high intelligence.

In your terminal, run:

ollama run deepseek-coder

Ollama will begin pulling the multi-gigabyte model from their registry. Go grab a coffee. Depending on your internet speed, this takes a few minutes.

Once the download finishes, Ollama will drop you straight into a terminal chat prompt. It's ready.

Type something like: "Write a Python script that uses BeautifulSoup to scrape the title of a webpage and handles request errors gracefully."

Hit enter. You will immediately see DeepSeek streaming the code back to you. Notice how fast it is? Because it's running locally, there is zero network latency. The tokens generate as fast as your GPU can calculate them.

I use this constantly for quick regex generation, bash scripting, and asking architecture questions without having to Alt-Tab into a browser window to open ChatGPT.

Chatting in the terminal is cool for a demo, but it's not practical for daily work. Because Ollama acts as a local API server (running on http://localhost:11434), you can plug DeepSeek into almost any AI tool.

For VS Code / Cursor alternatives:

Install an extension like Continue.dev or OpenCode in VS Code. Go to their settings, select "Ollama" as your provider, and type in "deepseek-coder" as the model. Now you have DeepSeek acting as your inline autocomplete and chat assistant right inside your codebase, completely offline.

For a ChatGPT-like Web UI:

If you want a beautiful web interface to use DeepSeek like ChatGPT, install Open WebUI via Docker. It automatically connects to your local Ollama instance and gives you chat history, code highlighting, and even the ability to upload documents for DeepSeek to read.

Running DeepSeek locally feels like having a superpower. You have one of the world's most capable coding AI models sitting on your hard drive, available 24/7, with no subscription fees, no rate limits, and total data privacy.

The gap between closed-source cloud models and open-source local models is shrinking every month. Setup Ollama today, and take back control of your AI workflow.