How does autoscaling architecture in ChatGPT inference clusters handle sudden traffic spikes?

Autoscaling adjusts the number of inference workers and GPUs dynamically based on traffic, queue depth, and latency signals.

ChatGPT-scale systems use autoscaling to handle unpredictable traffic spikes. The system continuously monitors metrics like request rate, queue depth, GPU utilization, and p95/p99 latency.

When demand increases, new inference workers and GPU nodes are provisioned. Scaling can be horizontal (adding nodes) or vertical (allocating more resources per node). Predictive autoscaling may also pre-warm capacity based on historical patterns.

The challenge is balancing fast scaling response with cost efficiency and avoiding oscillations (thrashing) in resource allocation.