Multi-node training bottleneck isn't compute anymore—it's network latency and coordination overhead.
Atlassian's infra team just dropped insights on this: most teams still treat inter-node comms as an afterthought when it should be architected as a first-class concern.
When you scale beyond single-node, GPU utilization drops hard if your network fabric can't keep up. Gradient sync, parameter servers, and collective ops (AllReduce, etc.) become the critical path.
This is why RDMA, NVLink across nodes, and topology-aware scheduling matter way more than raw TFLOPS at scale. Infra teams sleeping on this are leaving performance on the table.
Atlassian's infra team just dropped insights on this: most teams still treat inter-node comms as an afterthought when it should be architected as a first-class concern.
When you scale beyond single-node, GPU utilization drops hard if your network fabric can't keep up. Gradient sync, parameter servers, and collective ops (AllReduce, etc.) become the critical path.
This is why RDMA, NVLink across nodes, and topology-aware scheduling matter way more than raw TFLOPS at scale. Infra teams sleeping on this are leaving performance on the table.