Local AI for Coding: Set Up AI Code Completion That Actually Works

GitHub Copilot sends your code to Microsoft's servers. In 2026, local alternatives provide comparable quality with complete privacy. This guide sets up AI code completion in VS Code and Neovim using only local models that never leave your machine.

Option 1: Continue.dev (VS Code/JetBrains)

# Install Continue extension in VS Code
# Extensions → Search "Continue" → Install

# ~/.continue/config.json
{
  "models": [
    {
      "title": "Qwen 14B (local)",
      "provider": "ollama",
      "model": "qwen3.6:14b",
      "apiBase": "http://localhost:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen 7B (fast autocomplete)",
    "provider": "ollama",
    "model": "qwen3.6:7b"
  }
}

Option 2: Tabby ML Server

# Pull and run Tabby (self-hosted GitHub Copilot replacement)
docker run -it --gpus all   -p 8080:8080   -v $HOME/.tabby:/data   tabbyml/tabby serve   --model TabbyML/DeepseekCoder-6.7B   --device cuda

# Install Tabby VS Code extension and point to localhost:8080

Best Models for Code Completion

Model	VRAM	HumanEval	Best For
Qwen3.6 14B	9 GB	88%	General coding + chat ★
DeepSeek-Coder V2 16B	10 GB	85%	Code-only tasks
Starcoder2 15B	9 GB	78%	Fast inline completion
Qwen3.6 7B (fast)	5 GB	80%	Low-latency completion

Optimal Setup: Two-Model Architecture

For the best experience, use a small fast model for inline completions and a larger model for chat/explanations:

ollama pull qwen3.6:7b    # fast completions
ollama pull qwen3.6:32b   # thorough chat and code review

For hardware comparisons of GPUs optimized for low-latency code completion, see LLMHardware.io's coding setup guide.