Ollama
by ollama
Run Large Language Models Locally with Ollama
A Go-powered runtime that lets developers run LLMs like Llama, Mistral, and Gemma locally without cloud dependencies or API costs.
- 159,860+ GitHub stars
- Built with Go
- Simple CLI for downloading and running 50+ open-source language models
- MIT License license
About This Project
Ollama is a lightweight, efficient runtime built in Go that enables developers to download, run, and manage large language models directly on their local machines. It eliminates the need for cloud APIs, providing full control over model execution while maintaining privacy and reducing operational costs.
The tool supports an extensive collection of popular open-source models including Llama 3, Mistral, Gemma 3, DeepSeek-R1, Phi-4, and Qwen. With its simple CLI interface and optimized performance, developers can quickly switch between models, customize parameters, and integrate LLM capabilities into applications without complex setup or infrastructure management.
Ollama handles model quantization, memory management, and GPU acceleration automatically, making it accessible even for developers new to working with large language models. The project's massive community adoption (159K+ stars) reflects its reliability and ease of use for local AI development.
Whether you're building chatbots, content generators, code assistants, or experimenting with AI capabilities, Ollama provides a production-ready foundation for running state-of-the-art language models entirely offline with minimal configuration.
Key Features
- Simple CLI for downloading and running 50+ open-source language models
- Automatic GPU acceleration and memory optimization for efficient inference
- Model library including Llama 3, Mistral, Gemma 3, DeepSeek-R1, and Phi-4
- RESTful API for easy integration with existing applications and workflows
- Zero cloud dependencies - complete offline operation with full data privacy
- Built-in model quantization for reduced memory footprint and faster performance
- Cross-platform support for macOS, Linux, and Windows environments
How You Can Use It
Building privacy-focused AI applications that process sensitive data locally
Developing and testing LLM-powered features without incurring API costs
Creating offline-capable AI assistants and chatbots for desktop applications
Rapid prototyping and experimentation with different language models
Running AI workloads in air-gapped or restricted network environments
Building code completion and developer tools with local inference
Who Is This For?
Software developers, AI engineers, and teams building LLM-powered applications who need local model execution, cost control, and data privacy