"The future of inference is distributed, intelligent, and invisible. Here's where we're heading."
AI inference is undergoing a fundamental architectural shift. The centralized model—where all requests flow to a handful of mega-datacenters—is giving way to something more distributed, more intelligent, and more resilient. Here's what the next five years of AI inference look like.
Today, most AI inference happens in a small number of very large datacenters. This creates several problems:
Few mega-datacenters, global traffic routing, variable latency
Many edge locations, local traffic stays local, consistent latency
The evolution of AI inference will follow a predictable path:
Most workloads in major cloud regions. High latency for global users.
Inference spreads to more regions. Latency improves for major markets.
Smaller models run at the edge. Real-time applications become viable.
AI inference everywhere—devices, edge, cloud—seamlessly orchestrated.
Distributed inference unlocks applications that are impossible with centralized architectures:
We're building for the distributed future today. Our network is designed from the ground up to leverage edge infrastructure, intelligent routing, and global optimization.
The future of inference is distributed. The question isn't if we'll get there, but who builds the infrastructure to make it happen. We intend to be that infrastructure.