Decentralized Training Actually Works

Decentralized training actually works and the implications are significant. Training models across random computers scattered around the world. It's slow, expensive, but it works. The coordination overhead is insane. Syncing gradients across unreliable networks. Handling nodes that disappear mid training. Byzantine fault tolerance for parameter updates. Everything takes 10x longer and costs 10x more. But here's why it matters. If only big companies can train models, they control AI. Decentralized training breaks that monopoly. No single entity controls the process. No hidden backdoors or bias injection. The privacy benefits are underappreciated. Each node only sees a small slice of data. The complete dataset never exists in one place. You could train on medical records without any single computer seeing patient data. The models aren't quite as good as centralized training but they're good enough. And good enough with freedom beats perfect with control every time. We can train 7B parameter models today. Tomorrow it'll be 70B. The protocols are rough but improving. This is how AI training democratizes.