How does GPU utilization optimization influence cost efficiency in ChatGPT inference clusters?

Optimizing GPU utilization increases throughput per dollar by reducing idle GPU time and maximizing batch efficiency.

GPU utilization is a key metric in ChatGPT infrastructure. Low utilization means wasted compute resources, while high utilization improves cost efficiency. Techniques like dynamic batching, model parallelism, and overlapping compute with data transfer are used to maximize GPU usage.

Schedulers also reduce idle time by merging requests and preloading KV-cache. However, pushing utilization too high can increase latency due to queueing delays.

The goal is to find an optimal balance between throughput and latency constraints.