How does TensorFlow Serving handle high-throughput inference?

TensorFlow Serving optimizes inference using batching, model versioning, and request scheduling.

TensorFlow Serving loads models into memory and serves predictions via optimized gRPC/REST endpoints. It supports dynamic batching, which groups requests to maximize GPU utilization. Model versioning allows seamless rollout without downtime. Efficient request scheduling reduces latency under high load.