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Optimize AI network performance with distributed interconnection hubs

As AI scales, centralized data storage increases latency and cost while complicating compliance. A distributed AI architecture that relies on global interconnection hubs and direct, private connectivity places data and workloads near users, inference engines, clouds and partners. The result is faster inference, lower egress spend and simplified multicloud and multi-model operations.

What You'll Find Inside:

  • Learn how data locality affects latency and AI user experience. 
  • Discover hub-and-spoke patterns that align placement of data, training and inference. 
  • Understand low-latency connectivity options that cut egress and avoid cloud lock-in. 
  • See governance patterns that meet data privacy and sovereignty requirements. 
  • Map a practical rollout that scales from pilot to production across regions and clouds.
Moving into Equinix’s San Francisco facility gets us the proximity and closeness to our data and the most direct and optimized route possible to our cloud services.
Chris Palmer
Senior Manager of Advanced Technology Services, PCL Construction
Distributed AI