How does request queuing and scheduling affect ChatGPT latency under high load?

Request scheduling controls how incoming prompts are queued and prioritized, directly affecting latency and fairness.

In high-traffic ChatGPT systems, incoming requests are placed into queues before being assigned to GPU workers. Scheduling algorithms decide execution order based on priority, user tier, request size, and latency sensitivity.

Advanced schedulers use techniques like priority queues, weighted fair queuing, and dynamic batching to optimize GPU utilization while minimizing tail latency. Long requests may be split or delayed to prevent blocking shorter ones.

Poor scheduling can lead to queue buildup, increased p99 latency, and unfair resource allocation across users.