Distributed training bottlenecks everyone talks about: GPU memory limits, network bandwidth caps, node failures.
The silent killer nobody debugs: cluster formation itself.
If your cluster topology is wrong from the start, every single training run inherits that architectural debt. You're optimizing on top of a broken foundation.
Most devs burn cycles fixing symptoms (OOM errors, slow syncs, crashed nodes) without realizing the root cause is how nodes were grouped and connected in the first place.
Fix cluster formation strategy before you tune hyperparameters.
The silent killer nobody debugs: cluster formation itself.
If your cluster topology is wrong from the start, every single training run inherits that architectural debt. You're optimizing on top of a broken foundation.
Most devs burn cycles fixing symptoms (OOM errors, slow syncs, crashed nodes) without realizing the root cause is how nodes were grouped and connected in the first place.
Fix cluster formation strategy before you tune hyperparameters.