This diagram illustrates network bottleneck issues in large-scale AI/ML systems.
Key Components:
Left side:
- Big Data and AI Model/Workload connected to the system via network
Center:
- Large-scale GPU cluster (multiple GPUs arranged in a grid pattern)
- Each GPU is interconnected for distributed processing
Right side:
- Power supply and cooling systems
Core Problem:
The network interface specifications shown at the bottom reveal bandwidth mismatches:
- inter GPU NVLink: 600GB/s
- inter Server Infiniband: 400Gbps
- CPU/RAM/DISK PCIe/NVLink: (relatively lower bandwidth)
“One Issue” – System-wide Propagation:
A network bottleneck or failure at a specific point (marked with red circle) “spreads throughout the entire system” as indicated by the yellow arrows.
This diagram warns that in large-scale AI training, a single network bottleneck can have catastrophic effects on overall system performance. It visualizes how bandwidth imbalances at various levels – GPU-to-GPU communication, server-to-server communication, and storage access – can compromise the efficiency of the entire system. The cascading effect demonstrates how network issues can quickly propagate and impact the performance of distributed AI workloads across the infrastructure.
with Claude
Lechuck Park 