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Building a Local Copilot with Ollama and Continue

Cloud-based AI coding assistants offer incredible productivity, but they come with a major catch: your proprietary codebase, API keys, and sensitive internal logic are sent to third-party servers. For enterprises and privacy-conscious developers, this is a non-starter.

By combining Ollama (to run open-source models locally) and Continue (the open-source VS Code and JetBrains extension), you can build a lightning-fast, 100% private AI coding assistant that runs entirely on your own hardware with zero data leakage.

How Local AI Coding Assistants Work

The local AI stack decouples your IDE from cloud infrastructure by running inference directly on your local GPU or CPU:

  • Ollama Backend: Manages model weights, handles local hardware acceleration (CUDA/Metal), and exposes an OpenAI-compatible REST API on localhost:11434.
  • Continue Extension: Integrates directly into your editor's sidebar and editor windows, handling chat interactions, inline code completions (autocomplete), and codebase indexing.
  • Embedding & RAG: Indexes your local workspace files so the model can answer questions about your specific project context without leaking files externally.

Cloud Copilots vs. Local Ollama + Continue

FeatureCloud-Based CopilotsLocal Ollama + Continue
Data PrivacyCode transmitted to external servers100% offline and private (Zero data leaves your machine)
CostMonthly subscription per userFree and open-source (uses local compute)
Model ChoiceLocked to vendor-provided modelsSwappable (Llama 3, DeepSeek-Coder, Qwen, Codellama)
Internet RequirementRequired for every requestWorks completely offline (air-gapped environments)

Setup and Configuration Blueprint

Getting your local copilot running takes just a few steps. First, install Ollama and pull a powerful coding model like deepseek-coder or llama3:

Bash
Pulling a local coding model via Ollama
# Install and start Ollama, then run in your terminal:
ollama run deepseek-coder:6.7b-instruct

Next, install the Continue extension from the VS Code marketplace. Configure your ~/.continue/config.json to connect Continue to your local Ollama instance:

JSON
Continue extension configuration for Ollama
{
  "models": [
    {
      "title": "DeepSeek Coder 6.7B",
      "provider": "ollama",
      "model": "deepseek-coder:6.7b-instruct"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen 2.5 1.5B Codellama",
    "provider": "ollama",
    "model": "qwen2.5-coder:1.5b-instruct"
  }
}

Production Best Practices

Optimizing local inference performance requires balancing model size with available VRAM:

  • Match Models to Hardware: Use smaller 1.5B to 3B models (like Qwen2.5-Coder) for real-time tab autocomplete to keep latency under 100ms, and larger 7B/14B models for chat and refactoring.
  • Offload to GPU: Ensure Ollama is configured to run fully on your NVIDIA CUDA or Apple Silicon GPU rather than falling back to CPU memory.
  • Exclude Large Directories: Configure your Continue ignore settings to exclude node_modules, build folders, and large datasets from codebase indexing.