AI Interconnection for Hyperscalers: Why the Network Is Now the Bottleneck
If you are building hyperscale AI infrastructure and the network is part of the conversation, FD-IX.ai is worth talking to early
AI
How AI Traffic Affects the Data Center
AI infrastructure pushes data center networks much harder than traditional enterprise workloads ever did. GPU clusters constantly exchange traffic between nodes during training jobs, which keeps east-west links busy for long periods instead of short bursts toward the Internet.
Solving Data sovereignty through Interconnection
Data sovereignty is often treated like a legal checkbox, but the real control lies in the network design.
The One-Way Forward Neural Network:Feedforward Models Power Modern AI
That design is called a one-way forward neural network, more formally known as a feedforward neural network.
Your GPUs Are Waiting on the Network
Distributed training lives or dies on synchronization time.
Every millisecond between clusters compounds across epochs.
AI Infrastructure Is Not Traditional Peering
AI workloads do not behave like web traffic. There is no clean “user → server → response” loop. Instead, you have clusters of GPUs exchanging data constantly.