Every major AI code editor today — Copilot, Cursor, Windsurf — sends your code to a cloud server, runs it through a remote model, and sends suggestions back. For most developers, that works fine. But for a growing number of teams, it’s a dealbreaker.
If you’re writing code in a defense facility, a hospital system, a trading floor, or a robotics lab with no outbound network access, cloud-based AI coding tools simply don’t work. Not because of preference — because of policy, regulation, or physics.
That’s the problem GIDE was built to solve.
GIDE in One Sentence
GIDE is an AI code editor that runs a local large language model on your hardware, inside your editor, with zero cloud dependency, zero token costs, and zero telemetry.
Everything happens on your machine. Your code never leaves. There’s no API key to manage, no usage meter ticking, and no data exfiltration risk to mitigate.
How It Actually Works
Under the hood, GIDE runs Qwen 3.5 4B Coder through llama.cpp — a high-performance C++ inference engine that runs quantized models directly on consumer hardware. No Python environment, no Docker container, no CUDA toolkit to install. The model ships with the editor.
When you open GIDE and start coding, the model loads into memory and runs inference locally. Suggestions, completions, refactors, and agentic actions all execute on your CPU or GPU without ever touching a network socket.
The editor itself is built on Electron (a VS Code / code-oss fork), so the interface will feel immediately familiar to anyone who’s used VS Code. The AI layer sits on top with several systems working together:
- Agentic Plan-Gate System — the AI proposes a plan, you approve or reject each step before it executes. No unsupervised changes.
- Context Memory Server — structured external memory that persists across sessions. Not RAG-based retrieval — actual structured context the model references as it works.
- Universal Refactoring Engine — 9 AST primitives covering 21 languages via Tree-sitter. Rename, extract, inline, move — all with syntax-aware precision.
- Full MCP Client — connect to Model Context Protocol servers for extended tool use.
- 20 Safety Guards — constraints that prevent the agent from doing things like deleting files outside the project, running destructive shell commands, or modifying system paths.
- Sandboxed Shell — terminal execution happens inside a controlled environment.
- JSONL Audit Telemetry — every action the AI takes is logged locally in a machine-readable format for compliance review. The telemetry stays on your machine.
Who It’s Built For
GIDE targets six core audiences, all of them defined by environments where cloud AI either can’t work or shouldn’t work:
Defense and government contractors operating in air-gapped SCIFs or classified networks. These environments have no internet access by design. Cloud-based coding assistants are physically impossible to use.
Healthcare developers building on systems governed by HIPAA, where patient data exposure through a cloud API — even accidentally — creates regulatory liability.
Financial services teams in trading and compliance environments where code and data cannot leave the network perimeter, and every tool must pass security review.
Robotics and embedded engineers who develop in factory floors, field environments, or test facilities where connectivity is unreliable or nonexistent.
Privacy-focused independent developers who simply don’t want their code analyzed by a third-party cloud provider, regardless of that provider’s data policies.
Enterprise security teams evaluating AI coding tools and need one that produces a verifiable audit trail with no external data flow.
System Requirements
GIDE runs on all three major platforms:
- macOS: 13+ with Apple Silicon M2 or later, 16GB unified memory, 25GB storage
- Windows: 11 with Intel i7 or AMD Ryzen 5000+, NVIDIA RTX 3050+, 16GB RAM, 25GB storage
- Linux: Available as .deb, .rpm, and .AppImage
The hardware requirements reflect the reality of running a 4-billion parameter model locally. You need a reasonably modern machine — but not a workstation. A MacBook Pro M2 or a mid-range Windows laptop with a discrete GPU handles it well.
How It Compares to Cloud AI Editors
The comparison with tools like GitHub Copilot, Cursor, or Windsurf isn’t really about which is “better” — it’s about which model of AI assistance fits your constraints.
Cloud editors offer larger models (GPT-4, Claude, etc.), broader context windows, and more sophisticated multi-file reasoning. They’re excellent tools if your environment allows them.
GIDE trades model size for sovereignty. A 4B parameter local model won’t match a 200B cloud model on raw capability — but it will run in an environment where the cloud model can’t exist at all. For teams operating under those constraints, the choice isn’t between local and cloud. The choice is between local AI and no AI.
Try It
GIDE is currently in free Early Access. You can download it at generativeide.com/download and be running local AI-assisted coding in minutes.
No account required. No credit card. No phone-home. Just install and code.