ollama open source analysis

Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models.

Project overview

⭐ 158870 · Go · Last activity on GitHub: 2026-01-06

GitHub: https://github.com/ollama/ollama

Why it matters for engineering teams

Ollama addresses the practical challenge of integrating and running multiple open source large language models (LLMs) within engineering workflows. It offers a production ready solution for teams that need to deploy models like GPT-OSS, Gemma 3, and LLaMA variants in a consistent and manageable way. This makes it particularly suitable for machine learning and AI engineering teams focused on building scalable AI-driven applications. The project is mature enough for production use, providing a self hosted option that ensures data privacy and customisation. However, it may not be the best choice for teams seeking a fully managed service or those without the infrastructure to support self hosting, as it requires operational overhead and expertise in Go-based tooling.

When to use this project

Ollama is a strong choice when your team requires a flexible, open source tool for engineering teams to run multiple LLMs locally or on private infrastructure. Consider alternatives if you need a cloud-native managed service or simpler deployment without the need for extensive customisation.

Team fit and typical use cases

Machine learning engineers and AI specialists benefit most from Ollama as it enables them to experiment with and deploy various LLMs in production environments. It is commonly used in products that require natural language understanding, conversational AI, or custom model integration where control over the model environment is critical. This self hosted option for LLM deployment supports teams building AI features into enterprise applications or research platforms.

Best suited for

Machine Learning and AI Engineer

Topics and ecosystem

deepseek gemma gemma3 gemma3n go golang gpt-oss llama llama2 llama3 llava llm llms mistral ollama phi4 qwen

Activity and freshness

Latest commit on GitHub: 2026-01-06. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.