Collama - Run Ollama Models on Google Colab (Free, No Local GPU)

2026-03-22 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Novice, quick

Summary

The Collama project enables users to run Ollama models directly on Google Colab, providing a free cloud environment for experimenting with Large Language Models (LLMs) without requiring a local GPU. This minimal setup installs Ollama within Colab, supports models such as Llama, Qwen, DeepSeek, and CodeLlama, and exposes the Ollama API for integration with external tools. The project aims to simplify the process, addressing common issues found in other tutorials like complexity, outdated instructions, or missing steps for tunneling and API access. It offers a reproducible setup for various use cases, including testing coding models, building AI tools, running agents, and conducting prompt engineering experiments.

Key takeaway

For AI Engineers or Prompt Engineers seeking to experiment with LLMs without local GPU resources, Collama offers a streamlined solution. You should leverage this project to quickly set up and run Ollama models like Llama or CodeLlama on Google Colab, facilitating rapid prototyping and testing of AI applications or agent workflows. This removes friction often associated with cloud-based LLM experimentation.

Key insights

Collama simplifies running Ollama LLMs on Google Colab, enabling free cloud-based experimentation without a local GPU.

Principles

• Simplify complex setups
• Ensure reproducibility
• Provide API access

Method

Install Ollama in Google Colab, load desired models (e.g., Llama, Qwen), and expose the API via tunneling for external tool integration.

In practice

• Test coding models
• Build quick AI tools
• Run prompt engineering experiments

Topics

• Ollama
• Google Colab
• Large Language Models
• LLM Deployment
• Prompt Engineering

Code references

• 0x1881/collama

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.