Codex + Ollama = Free Unlimited Coding AI

Key Concepts

• Codex: An AI coding agent designed to build, edit, review, and ship software.
• Ollama: A tool that enables the local execution of open-source Large Language Models (LLMs) on a user's machine.
• Local AI Workflow: The process of running AI models on local hardware rather than relying on cloud-based APIs, ensuring privacy and zero cost.
• Model Quantization: A technique to reduce the precision of model weights (e.g., 4-bit) to make them run on consumer-grade hardware.
• Parameter Count: A measure of model size (e.g., 4B for 4 billion parameters), which dictates the hardware requirements for local hosting.

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1. Integration Overview

The integration of Ollama into the Codex application allows users to run open-source models locally for free. This development enables developers to utilize Codex’s advanced coding agent capabilities—such as building, editing, and reviewing code—without the need for paid cloud subscriptions.

2. Prerequisites and Compatibility

• Operating Systems: Currently supported on macOS and Windows; Linux support is expected in the near future.
• Software Requirements:
  • Codex application installed.
  • Ollama (version 0.24 or higher).
• Hardware Assessment: Users are encouraged to use the website “Can I Run AI Locally” to input their GPU, VRAM, and RAM specifications to determine which model sizes (e.g., 4B, 8B) their hardware can support.

3. Step-by-Step Implementation Process

1. Install Ollama: Ensure the latest version (0.24+) is installed via the terminal or the official installer.
2. Select and Download a Model:
   • Identify a model (e.g., Gemma 4 or Qwen 3.6) via the Ollama model library.
   • Use the command ollama run [model_name]:[variant] in the command prompt to download the specific version (e.g., gemma4:4b).
3. Launch Codex with Ollama:
   • Open a new command prompt.
   • Execute the command: ollama launch codex-app.
   • Select the locally installed model from the list provided in the terminal.
4. Verification: The Codex app will launch, indicating it is powered by the local Ollama model.

4. Functionality and Use Cases

• Visual Editing: Codex can load local servers and sites via its built-in browser, allowing users to annotate pages and make real-time changes through the chat interface.
• Code Iteration: Users can review code, leave comments, and iterate on projects entirely within the local workspace.
• Example Application: The video demonstrates generating a SaaS landing page using a 4-billion-parameter Gemma model, which is then rendered successfully in an HTML viewer.

5. Managing Configurations

• Restoring Defaults: If a user wishes to revert to the standard Codex experience (or a previous plan), they can run the command: ollama launch codex-app --restore.
• Cloud vs. Local: While local hosting is free, users can still opt for paid cloud-based models (like Nemotron 3 or GLM 5.1) if they have an Ollama Cloud subscription, which may offer higher performance for complex tasks.

6. Synthesis and Conclusion

The integration of Ollama with Codex represents a significant shift toward accessible, local AI development. By removing the cost barrier and allowing users to leverage their own compute, this workflow empowers developers to build software with privacy and flexibility. The ability to run models like Gemma 4 locally within a professional-grade coding agent provides a robust, free alternative to traditional cloud-dependent AI coding tools.