I was testing a small routing scenario around opg this week and ran into something that changed how I think about decentralized AI infrastructure.
One request kept missing its latency target. At first I assumed the nearest inference node would always be the fastest option. It wasn’t.
The closest node had to load the model first, while another node farther away already had the model warm and was sitting mostly idle. The longer network path ended up delivering the result faster.
That made me realize node placement isn’t really a geography problem. It’s a coordination problem. Distance matters, but so do model availability, queue pressure, GPU capacity, and failure independence.
What I find interesting about OpenGradient is that a network can look highly distributed on a map while still sharing hidden dependencies underneath. Two nodes in different regions might still rely on the same operator or cloud provider.
I’m holding a small opg position, and this is one of the things I’m watching closely. The next nodes added to the network may matter more than the total number of nodes.