How to Run DeepSeek Coder Locally on Windows 11 with Ollama: Complete 2026 Setup Guide

Cloud-based AI coding assistants like GitHub Copilot and Claude Sonnet are undeniably powerful, but they present two major drawbacks for serious developers: subscription fatigue and privacy concerns regarding proprietary enterprise codebases. Fortunately, running a high-performing open-source coding model locally on Windows 11 is now easier than ever thanks to the lightweight Ollama runtime and DeepSeek Coder.

Setting up local AI coding agents provides zero-latency code completion without sending confidential code to third-party cloud servers.

Why Choose DeepSeek Coder for Local Development?

DeepSeek Coder is trained from scratch on 2 trillion tokens encompassing over 80 programming languages. Benchmark evaluations demonstrate that DeepSeek-Coder-Base-33B and its instruction-tuned variants outperform OpenAI’s GPT-3.5-Turbo across HumanEval, MBPP, and multi-language evaluation suites.

When running the quantized 6.7B model, developers obtain instantaneous fill-in-the-middle (FIM) code suggestions, automated unit test generation, and docstring writing—all without a single byte of telemetry leaving their local hard drive.

Hardware Requirements & Minimum Specs for Windows 11

Before proceeding with installation, verify that your machine meets these hardware criteria:

• OS: Windows 11 (64-bit, 22H2 or newer) with WSL2 enabled.
• Processor: Intel Core i5/i7 (10th gen+) or AMD Ryzen 5000+ with AVX2 instruction support.
• System RAM: Minimum 16GB DDR4/DDR5 (32GB strongly recommended for 33B models).
• Graphics Card (Optional): Nvidia RTX 3060 (6GB/12GB) or higher with CUDA 12.0+ for accelerated generation speeds.
• Disk Storage: At least 15GB free SSD storage (NVMe preferred for fast model weight loading).

Step 1: Install Ollama on Windows 11

Ollama provides a native Windows installer that automatically configures Windows background services and GPU hardware acceleration:

1. Visit the official Ollama website and download OllamaSetup.exe for Windows.
2. Run the installer. It will set up the binary in %LOCALAPPDATA%\Programs\Ollama and add it to your system PATH.
3. Open a standard Windows PowerShell or Terminal window and verify the installation:

# Verify Ollama service is active
ollama --version

Step 2: Pull and Run DeepSeek Coder via Terminal

Once Ollama is initialized, you can pull the model weights directly from the registry. For most developer laptops with 16GB RAM, the 6.7B instruct model represents the perfect sweet spot between velocity and reasoning:

# Pull the 6.7B Instruct model (~4.0 GB download)
ollama pull deepseek-coder:6.7b-instruct

# Launch an interactive terminal chat session
ollama run deepseek-coder:6.7b-instruct

You can test the interactive session by typing a prompt like: “Write a Python script using asyncio to scrape HTTP status codes concurrently.” You will see real-time streaming output generated directly by your machine’s CPU/GPU.

Step 3: Connect Local DeepSeek Coder to VS Code (Free Copilot Alternative)

Having a command-line chat model is helpful, but the real power comes from embedding DeepSeek Coder directly into your code editor for tab-autocomplete and inline code refactoring:

1. Open Visual Studio Code.
2. Navigate to the Extensions marketplace (Ctrl + Shift + X) and search for Continue (by Continue.dev).
3. Click Install. Once installed, an icon appears in your left activity bar.
4. Open the Continue configuration file (~/.continue/config.json) and add your local Ollama model definition:

{
  "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 in VS Code will trigger your private, zero-latency DeepSeek assistant to write, refactor, or explain code without incurring cloud token charges!

Performance Optimization Tips for Windows Users

To squeeze maximum tokens-per-second out of consumer hardware, apply these tuning tweaks in PowerShell:

• Keep Model Weights in Memory: Set the keep-alive flag so Ollama doesn’t unload the weights from RAM after 5 minutes of inactivity:

  $env:OLLAMA_KEEP_ALIVE="24h"

• Bind to All Local Interfaces: If you want to connect from a separate laptop or Docker container on your local Wi-Fi, set:

  $env:OLLAMA_HOST="0.0.0.0:11434"

• Allocate Dedicated CPU Threads: If running on CPU only, allocate 75% of your physical CPU cores to prevent UI stutter while rendering complex code trees.