Why do TensorFlow distributed systems become unstable when scaling beyond a certain number of nodes?

Instability arises from communication overhead, synchronization delay, and non-linear scaling inefficiencies.

As TensorFlow scales across more nodes, communication cost (especially all-reduce gradient synchronization) increases faster than compute gains. Network topology, bandwidth contention, and straggler effects create non-linear scaling degradation. At large scale, synchronization dominates computation, making training inefficient or unstable.