Why do TensorFlow inference systems fail under high QPS despite model optimization?

Failures occur due to CPU/GPU saturation, queue buildup, and batching inefficiencies.

Even optimized models fail under high queries per second because inference systems are bounded by hardware throughput, memory bandwidth, and request queue management. Without dynamic batching or load shedding, request queues grow, latency spikes, and eventually system timeouts occur. TensorFlow Serving helps but must be tuned properly.