In this session, you’ll discover how to turn it into a private AI powerhouse — without cloud dependencies, subscriptions, or data leaving your device. We’ll explore Small Language Models, LM Studio, MacWhisper, and AnythingLLM, and walk through practical examples for local transcription, document analysis, and fast on-device reasoning. By the end, you’ll know exactly what to play with between Christmas and New Year.

Learn how to turn a Mac into a private AI powerhouse without cloud dependencies or data leaving the device. Explore Small Language Models, LM Studio, MacWhisper, and AnythingLLM, with practical examples for local transcription, document analysis, and fast on-device reasoning.

Overview of how Small Language Models (SLMs) can solve business problems and boost device intelligence, with a comparison to Large Language Models (LLMs). Focus on fine-tuning and customization, edge deployments for real-time processing, and a cloud-to-edge deployment roadmap using Azure to maintain IP ownership and integration. Includes perspectives on leveraging SLMs to revolutionize devices and mentions Microsoft’s Student Ambassador Program energizing students to advance intelligent tech.