How does batching strategy impact throughput and latency trade-offs in ChatGPT inference systems?

Batching improves GPU utilization and throughput but can increase individual request latency if not carefully balanced.

Batching combines multiple inference requests into a single GPU execution step, improving hardware utilization. However, larger batch sizes increase queueing delay for individual requests.

ChatGPT systems use dynamic batching where requests are grouped within a short time window. Advanced schedulers balance batch size and latency targets to maintain p95/p99 latency constraints.

The tradeoff is between throughput efficiency and real-time responsiveness.