This hands-on workshop introduces participants to a practical and increasingly important approach to artificial intelligence: running AI systems locally on their own computer. While many AI tools today operate through cloud-based services, local AI offers an alternative that provides greater control over data, improved privacy, and independence from changing service policies or subscription costs.

Participants will gain a clear understanding of how local Large Language Models (LLMs) operate and how they can be deployed as a personal AI platform. The workshop explains the essential components of a local AI system, including hardware considerations, model selection, runtime environments, and system configuration, using clear explanations that make the topic accessible even to those without a deep technical background.

Through guided demonstrations and step-by-step implementation, participants will install and configure a local AI environment using modern tools designed to simplify the process. They will learn how to run and interact with a local language model, explore its capabilities, and evaluate the trade-offs between model size, performance, and hardware requirements.

In addition to technical setup, the workshop will discuss when local AI is appropriate and when cloud-based solutions remain preferable. Participants will explore real-world use cases such as document analysis, research assistance, knowledge organization, and private AI experimentation, while also developing realistic expectations about system performance and practical limitations.

By the end of the session, participants will have a clearer understanding of how to design and operate their own private AI platform, empowering them to experiment with generative AI in a controlled and independent environment. When hardware requirements are met, participants will leave the workshop with a functioning local AI system running on their own computer, providing a foundation for further exploration, experimentation, and integration into personal or professional workflows.