By skyforbes 
One of the most overlooked parts of AI architecture is the network layer. Everyone talks about GPUs, batch sizes, model weights, and inference speed but none of that matters if your data pipeline is bottlenecked by the wrong networking design.
This post breaks down some interesting points about how AI traffic actually behaves: east-west vs. north-south patterns, how distributed training stresses bandwidth differently than inference, and why private service connectivity matters when your models pull from multiple data sources. It also touches on why hybrid AI workloads (on-prem + cloud) live or die based on latency and throughput trade-offs.
If you're interested in the infrastructure side of AI scale, this blog has a solid technical breakdown: Cloud Networking for AI Workloads.
Curious how others here are optimizing network paths for training jobs; are you leaning more on peering, interconnect, or fully VPC-native designs?