How does distributed serving orchestration work in ChatGPT production architecture?

Distributed serving orchestrates multiple model replicas across GPUs/regions using load balancing, routing, and autoscaling.

ChatGPT-scale systems are deployed as distributed inference services where multiple replicas of the model run across GPU clusters. A request first hits an API gateway, then a router decides which region, cluster, and replica should handle it based on latency, load, and availability.

Inside each cluster, load balancers distribute requests to model workers. These workers may use KV caching, batching, and tensor-parallel inference. Autoscaling systems adjust replica count based on GPU utilization and request queue depth.…