What is distributed inference scheduling in large-scale ML serving systems?

Distributed inference scheduling allocates inference requests across multiple compute nodes to optimize latency and throughput.

Inference schedulers distribute requests based on load, model size, hardware type, and latency constraints. Techniques include dynamic batching, priority queues, and load-aware routing. Advanced schedulers also consider GPU memory fragmentation, warm model placement, and locality to minimize data transfer overhead. This is essential in large-scale systems serving billions of requests.