How to Run DeepSeek Coder Locally on Windows 11 with Ollama (Zero GPU Required)

Cloud-based AI coding assistants like GitHub Copilot and Claude Sonnet are undeniably powerful, but they present two major compromises for serious developers: continuous monthly subscription fees ($10 to $20/month) and enterprise code privacy concerns.

If you don't want your private scripts or proprietary enterprise source code being sent to third-party cloud servers, running an open-source model locally on Windows 11 is the ultimate solution.

In this hands-on guide, we will walk through running DeepSeek Coder 6.7B locally on Windows 11 using the lightweight Ollama engine—even if you don't have an expensive dedicated Nvidia graphics card.

⚡ Originally published with comprehensive benchmarks on Quikro Tech Blog: Run DeepSeek Coder Locally.

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Why Choose DeepSeek Coder?

DeepSeek Coder is trained on 2 trillion tokens encompassing over 80 programming languages. Benchmark evaluations demonstrate that the DeepSeek-Coder-6.7B-Instruct model outperforms standard GPT-3.5-Turbo in HumanEval and MBPP coding challenges.

Hardware Requirements for Windows 11

• OS: Windows 11 (64-bit) with WSL2 enabled.
• RAM: 16GB RAM recommended for smooth CPU inference.
• GPU (Optional): Any Nvidia RTX 3060+ card (6GB/12GB) will boost speeds up to 45 tokens/second. On CPU alone, expect 18-25 tokens/second.
• Storage: ~10GB free SSD space.

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Step 1: Install Ollama on Windows

1. Download the Windows installer (OllamaSetup.exe) from the official Ollama site.
2. Run the setup. Ollama runs as a background service in Windows and adds the ollama CLI to your PATH.
3. Open Windows PowerShell and test:

ollama --version



Step 2: Download & Run DeepSeek Coder
Run this single command in PowerShell to pull and launch the model:

powershell
ollama run deepseek-coder:6.7b-instruct
Ollama will download the quantized model weights (~3.8 GB) and immediately launch an interactive chat terminal. You can test it with:

"Write a Python script using asyncio and aiohttp to scrape multiple endpoints concurrently with rate limiting."

Step 3: Connect to VS Code for Inline Autocomplete
Having a CLI chatbot is neat, but the real magic is tab-autocomplete in your code editor.

Open Visual Studio Code.
Install the Continue extension from the marketplace (Continue.dev).
Open ~/.continue/config.json and configure Ollama:
json
{
  "models": [
    {
      "title": "DeepSeek Coder 6.7B (Local)",
      "provider": "ollama",
      "model": "deepseek-coder:6.7b-instruct",
      "apiBase": "http://localhost:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "DeepSeek Coder 1.3B Autocomplete",
    "provider": "ollama",
    "model": "deepseek-coder:1.3b-base",
    "apiBase": "http://localhost:11434"
  }
}
Now pressing Ctrl + I triggers inline edits and code generation directly inside VS Code with zero latency and zero API cost.

Performance Pro-Tips for Windows Users
To ensure the model stays loaded in RAM and doesn't get evicted after 5 minutes of idle time, run this in PowerShell:

powershell
$env:OLLAMA_KEEP_ALIVE="24h"
For more benchmarks and full memory configuration options, check out the comprehensive walkthrough on Quikro Blogs: Run DeepSeek Coder Locally.

Built by Quikro — Engineering high-velocity SaaS, AI automation, and technical resources.

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