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.

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.

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. --- ### 5️⃣ Most Important: Canonical URL Set Karna (Backlink Credit Ke Liye) Dev.to editor ke bottom me **⚙️ (Settings gear icon)** hota hai (ya "Change post settings"): 1. **Canonical URL** field par click karein. 2. Waha yeh link paste kar dein:


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https://blogs.quikro.com/run-deepseek-coder-locally-windows-11-ollama-guide/