You’ve read the reports: DeepSeek is changing the AI landscape. But the real power of DeepSeek isn't using their website; it's running it offline on your own hardware.

Why run locally?

Privacy: Your code and proprietary data never leave your computer.
Cost: $0.00 per month.
No Internet: Code on a plane, in a bunker, or during an API outage.

With DeepSeek V4 on the horizon, now is the perfect time to set up your local environment. In this guide, we will use the current stable versions (V2/V3) to get your rig ready for the V4 drop.

1. Hardware Requirements: Can Your Rig Handle It?

Before we touch the terminal, check your specs. Running LLMs depends heavily on VRAM (Video RAM) and System RAM.

Model Size	Min RAM (Mac/PC)	Recommended GPU	Perfect For...
DeepSeek 7B (Lite)	8GB	RTX 3060 / M2 Air	Chat & Basic Logic
DeepSeek 33B (Pro)	24GB	RTX 4090 / M3 Max	Serious Coding
DeepSeek 67B (Max)	64GB+	Dual A6000 / Mac Studio	Research / Data Science

Mobile User? If you are eyeing the Samsung S26 Ultra with Snapdragon 8 Elite Gen 5, you will soon be able to run the 7B model natively on your phone's NPU!

2. The Easiest Method: Using Ollama (CLI)

In 2026, Ollama remains the gold standard for running local models on Linux, Mac, and Windows.

Step 1: Install Ollama

Download it from the official site (ollama.com). Once installed, open your terminal (Command Prompt).

Step 2: Pull the Model

Type the following command. We will use the current stable version to test your system.

        
          bash
          
        
      # Run the stable coding model (Works Now):
ollama run deepseek-coder-v2

# ⚠️ PRO TIP: When V4 releases, simply run:
# ollama run deepseek-coder-v4

Wait for the download (approx 4GB for the standard model). Once done, you can chat with it directly in the terminal.

3. The Developer Setup: DeepSeek in VS Code

This is the "Killer App". Imagine having GitHub Copilot, but it's free, private, and runs on your GPU.

Install the Extension: Search for "Continue" in the VS Code Marketplace.
Configure the Provider: Open the extension settings (config.json) and add Ollama as your provider.

Here is the configuration code you need:

        
          json
          
        
      {
  "models": [
    {
      "title": "DeepSeek Local",
      "provider": "ollama",
      "model": "deepseek-coder-v2",
      "apiBase": "http://localhost:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "DeepSeek Small",
    "provider": "ollama",
    "model": "deepseek-coder-v2"
  }
}

Once V4 is released, simply change the "model" line from v2 to v4 in this file.

4. The "No-Code" Method: LM Studio

If you prefer a graphical interface over terminals, download LM Studio.

Open LM Studio.
Click the Search icon (Magnifying glass).
Type text deepseek-coder.
Look for files ending in text .gguf (we recommend "Q4_K_M" quantization for the best balance).
Click Download and then "Load Model" to start chatting.

5. Performance Tweaks (Context Window)

By default, local runners often limit context to 4096 tokens to save RAM. DeepSeek supports much larger contexts. If you have enough RAM (32GB+), you can increase this to handle large files.

In Ollama, create a file named Modelfile:

        
          dockerfile
          
        
      FROM deepseek-coder-v2
PARAMETER num_ctx 32768

Then create the custom model in your terminal:

        
          bash
          
        
      ollama create deepseek-long -f Modelfile

1. Hardware Requirements: Can Your Rig Handle It?

2. The Easiest Method: Using Ollama (CLI)

Step 1: Install Ollama

Step 2: Pull the Model

3. The Developer Setup: DeepSeek in VS Code

4. The "No-Code" Method: LM Studio

5. Performance Tweaks (Context Window)

Conclusion: You Own the Intelligence

Frequently Asked Questions (FAQ)

1. Why is my computer slow when running this?

2. Is DeepSeek really free?

3. Can I run this on a standard laptop?

Alyvro

Alyvro

Alyvro

Alyvro