How does dynamic batching with token-aware scheduling improve GPU utilization in ChatGPT?

Token-aware scheduling groups requests based on token length to maximize GPU utilization while minimizing latency imbalance.

Traditional batching groups requests by count, but ChatGPT inference benefits from token-aware batching because requests vary widely in length. Token-aware scheduling groups requests with similar token budgets to reduce padding inefficiency.

Dynamic batching systems continuously collect incoming requests within a time window and form optimal batches based on token length, GPU memory constraints, and latency targets.

This improves throughput and reduces wasted computation from padding shorter sequences to match longer ones.