AI shouldn’t be limited to those with access to expensive hardware. This episode explores how to break down barriers by running massive AI models on "crappy machines"—affordable, low-spec devices. Clever techniques like quantization, pruning, model distillation might not be enough. With edge offloading, we can make state-of-the-art AI accessible to hobbyists, researchers, and innovators everywhere. By democratizing AI, we can empower individuals and small teams to experiment, create, and solve problems without needing deep pockets or enterprise-grade resources.   AI for everyone, on everything. Let’s make it happen.     ✨ Connect with us! 📩 Newsletter: https://datascienceathome.substack.com 🎙 Podcast: Available on Spotify, Apple Podcasts, and more. 🐦 Twitter: @DataScienceAtHome 📘 LinkedIn: https://www.linkedin.com/in/fragadaleta/ Instagram: https://www.instagram.com/datascienceathome/ Facebook: https://www.facebook.com/datascienceAH LinkedIn: https://www.linkedin.com/company/data-science-at-home-podcast Discord Channel: https://discord.gg/4UNKGf3   NEW TO DATA SCIENCE AT HOME? Welcome! Data Science at Home explores the latest in AI, data science, and machine learning. Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions. Learn more at https://datascienceathome.com   SEND US MAIL! We love hearing from you! Send us mail at: hello@datascienceathome.com Don’t forget to like, subscribe, and hit the 🔔 for updates on the latest in AI and data science!