I built a private LLM on my home PC using a USB drive — it only knows what I put on it

I built a private LLM on my home PC using a USB drive — it only knows what I put on it Jorge Aguilar / MakeUseOf By  Jorge Aguilar Published May 30, 2026, 3:00 PM EDT Aggy is a writer and editor who has worked for many high-traffic digital publications. He's a technology and gaming fanboy who has been a writer, editor, consultant, and computer animator. Sign in to your MakeUseOf account Add Us On Summary Generate a summary of this story follow Follow followed Followed Like Like Thread Log in Here is a fact-based summary of the story contents: Try something different: Show me the facts Explain it like I’m 5 Give me a lighthearted recap When you feed sensitive code or research into a cloud-based AI, you lose control over where that information travels. It might seem like a small trade-off for the convenience of a smart assistant, but you are effectively handing your data over to servers you don't own. It's a risk that most people accept without a second thought. However, you don't have to choose between advanced language models and the safety of your own private files; just switch to a local LLM . Related I’ll never pay for AI again AI doesn’t have to cost you a dime—local models are fast, private, and finally worth switching to. Posts 7 By  Yadullah Abidi Privacy risks of cloud processing Your data is never as safe as you think it is when you use the cloud Jorge Aguilar / MakeUseOf When you send personal questions and code snippets to an external server, you are sending your private text, intellectual property, and sensitive information over the internet to computers owned by someone else. It's easy to forget that because it feels like your own home computer. Running your tasks through these centralized cloud systems creates major risks for your data privacy and control. Outside companies process your private information on machines you don't manage, which is a massive security vulnerability. Since your data can be passed along to outside partners within th