Run your own AI (but private)

Introducing private AI as a way to run AI locally on your computer without sharing data with external companies. Setting up this system is easy and free, taking about five minutes. The video also teases advanced features like connecting personal notes and documents to the AI for personalized responses.

Exploring AI models from Hugging Face with over 505,000 models available. Details on the LAMA Two AI model, its pre-training process using 2 trillion tokens of data, 6,000 GPUs for training, and costs associated with training. Mention of uncensored versions of AI models.

Instructions on setting up the Llama AI model on different operating systems like macOS, Linux, and Windows (coming soon). Installation process using a curl command for macOS and Linux, with additional steps for Windows users to set up WSL (Windows Subsystem for Linux) for installation.

Running the Llama Two AI model on a computer, showcasing the speed improvement when using GPUs. Demonstration of running the model on Linux virtual machines and the difference between CPU and GPU performance.

Explanation of fine-tuning AI models on private data for specific use cases like help desks or code troubleshooting. Details on the process of fine-tuning with VMware examples and the limited examples needed for fine-tuning compared to initial training.

Introduction to using RAG (Retrieval-Augmented Generation) to connect AI models with databases for more accurate and context-aware responses. Mention of how VMware and Nvidia provide tools for fine-tuning AI models with Intel and IBM collaborations for comprehensive AI solutions.

Discussion on setting up a private GPT (Generative Pre-trained Transformer) system for individual use outside of VMware's private AI solution. Instructions for installation, including a mention of limitations without GPU support.