Why do TensorFlow inference systems require load balancing even when using identical models?

Because request distribution, hardware variance, and batching efficiency differ across replicas.

Even identical TensorFlow models behave differently under load due to hardware heterogeneity, cache locality, and request patterns. Load balancing ensures even distribution of traffic so no single replica becomes a bottleneck. Without it, latency spikes and throughput degradation occur.