Distributed AI compute • heat recovery • water efficiency
EDGE AI • HEAT RECOVERY • WATER EFFICIENCY • DISTRIBUTED RESILIENCEEDGE AI • HEAT RECOVERY • WATER EFFICIENCY • DISTRIBUTED RESILIENCE
Network architecture

Distributed Edge AI

Instead of depending on one enormous campus, workloads can be routed across many smaller nodes located closer to users and integrated with existing commercial infrastructure.

World distributed edge network

Resilience through distribution.

The network concept combines geographic diversity, workload migration and local low-latency capacity. The published figures are engineering design targets, not guarantees of service.

<2 ms

Latency target

Target sub-2 ms within appropriate local edge zones.

99.999%

Availability target

A five-nines network design objective through redundancy and automated failover.

What a node can reuse.

Electrical service

Use existing commercial capacity where technically suitable.

Heat demand

Pair compute with hot-water or process-heat loads.

Building footprint

Retrofit existing sites rather than build every node from scratch.

Connectivity

Use diverse fiber and carrier routes to support redundancy.