Why do TensorFlow distributed systems fail when network latency fluctuates?

Fluctuating latency causes synchronization delays, gradient staleness, and worker idle time.

Distributed TensorFlow training relies on communication-heavy operations like all-reduce. If network latency is inconsistent, synchronization barriers are delayed, causing slow workers to block fast ones. This reduces GPU utilization and increases training time. In asynchronous setups, it leads to stale gradients, reducing convergence stability.