AI inference autoscaling adds or removes serving resources as request volume, queue depth or latency changes. Systems may scale prefill and decode workers separately because those stages have different resource requirements.
Model workers can be slow and expensive to start, so autoscaling must anticipate demand and preserve useful caches where possible. Policies also need limits that control cost and prevent unstable cycles of rapid expansion and contraction.
ELI5
AI inference autoscaling automatically adds or removes model-serving resources as demand changes. It watches signals such as request volume, queue length and response delay.
For example, a service can start more model workers before a daily traffic peak and remove them after demand falls. Because workers may take time to load large models, the policy needs prediction, cost limits and protection against rapid up-and-down cycles.
