In this video I break down every major way to run open source AI models, because there’s still this common misconception that you need fancy hardware, deep infra knowledge, or a bunch of code to make any of it work. That might’ve been true a few months ago, but not anymore. Open source is where the industry is headed, and the models are now genuinely competitive with closed source—plus you get full control over where they run (local, edge, private cloud), they’re customizable (fine-tuning, guardrails, architecture tweaks), and they’re way cheaper long-term, especially at scale. I walk through four main categories and rank them from easiest to hardest: (1) running models locally (like using Ollama in literally a couple minutes, or calling it from your own code via localhost:11434, or running 24/7 on something like a Mac Mini), (2) browser/hosted playgrounds for quick experimentation (Arena, Groq, Hugging Face Spaces), (3) managed inference APIs for building apps fast without touching infrastructure (Together, Fireworks, Groq), and (4) VPS setups where you want more control and privacy (SSH in, run models, use Docker, optionally rent GPUs hourly via RunPod/Vast.ai). Then I add two bonus advanced categories—managed cloud for real scaling/compliance, and on-device/edge for shipping models inside apps—because I think edge is going to get big soon. My goal is that after this, you can pick the workflow that matches your constraints and ship.