I Replaced GitHub Copilot With a Local Setup. Here’s What Nobody Tells You

2026-04-20 · Source: Artificial Intelligence in Plain English - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

The author replaced their GitHub Copilot subscription with a local AI coding assistant setup, leveraging Gemma 4 and Ollama. This self-hosted solution runs entirely on the user's machine, offering privacy by keeping code offline, eliminating subscription costs, and providing total control over the model's behavior and environment. The article highlights Gemma 4's significant capability jump over previous open models, citing a Codeforces ELO increase from 110 to 2,150. The setup requires hardware ranging from 8GB RAM (CPU only) for basic tasks to a recommended 12-16GB VRAM GPU (e.g., RTX 3080/4070) for optimal performance, with the author using an RTX 4070 Ti for sub-3-second responses. The setup process involves four steps: installing Ollama, pulling the Gemma 4 model, starting the Ollama server, and performing a sanity check, taking approximately 15 minutes.

Key takeaway

For AI Engineers and Software Engineers concerned about code privacy, recurring costs, or control over their AI coding tools, consider implementing a local coding assistant with Gemma 4 and Ollama. This setup provides a robust alternative to cloud services like GitHub Copilot, offering offline operation and eliminating subscriptions. You can achieve fast responses and maintain full ownership of your development environment, enhancing security and reducing long-term expenses.

Key insights

Local AI coding assistants using Gemma 4 and Ollama offer a private, cost-effective, and controllable alternative to cloud-based tools.

Principles

• Prioritize privacy by keeping proprietary code offline.
• Eliminate recurring costs with a one-time hardware investment.
• Gain full control over model behavior and environment.

Method

Install Ollama, pull a Gemma 4 model (e.g., gemma4:27b), start the Ollama server, and integrate with an IDE extension like Continue.dev for an offline coding assistant.

In practice

• Use Gemma 4 for code generation, debugging, and refactoring.
• Target 12-16GB VRAM GPU for optimal local performance.
• Integrate with Continue.dev for VS Code/JetBrains functionality.

Topics

• Local AI Assistant
• Gemma 4
• Ollama
• GitHub Copilot Alternative
• Code Privacy

Code references

• features/copilot

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence in Plain English - Medium.