Why do TensorFlow models behave unpredictably under heavy concurrency in inference systems?

High concurrency causes thread contention, batching inefficiency, and resource saturation.

Inference systems in TensorFlow must handle concurrent requests efficiently. Under high load, thread pools saturate, GPU batching becomes inefficient, and memory bandwidth becomes a bottleneck. If dynamic batching is not tuned properly, latency spikes occur and throughput degrades non-linearly.