How does memory-aware model scheduling prevent GPU OOM in ChatGPT inference clusters?

Memory-aware scheduling prevents GPU out-of-memory errors by predicting memory usage and allocating requests accordingly.

ChatGPT inference requires careful memory management due to KV-cache growth, activation storage, and batching overhead. Memory-aware schedulers estimate GPU memory usage before executing requests.

If predicted usage exceeds available memory, requests are delayed, split, or routed to other GPUs. Some systems also compress KV-cache or reduce batch size dynamically.

This ensures system stability and avoids catastrophic GPU crashes caused by memory overcommitment.