Securely Store LLM Locally: Prepare for Any Scenario

In this video, Matthew Berman demonstrates how to securely store a large language model (LLM) locally to have access to the entirety of human knowledge in case of emergencies. He discusses the importance of preserving knowledge for scenarios such as temporary or permanent internet outages, government restrictions on AI, and power loss. The video provides a step-by-step guide on choosing the right model, downloading it, storing it securely, running inference, and building redundancy. Matthew recommends using LM Studio for its user-friendly interface, and also mentions alternatives like Olama and TextGen Web UI. He explains different storage options, including solid-state drives (SSD), tape drives, and Blu-ray discs, and how to protect these drives from fire, interference, and moisture. The video concludes with tips on maintaining the stored models and software, ensuring the latest updates, and checking battery health for backup power. Matthew emphasizes the ease and minimal cost of preparing for such scenarios and invites viewers to share their ideas for further improvements.