CPU-based autoscaling often reacts too late or to the wrong signal for GPU inference.
CAPACITY ENGINEERINGAI SRE
Autoscaling AI Inference
AI autoscaling adjusts serving capacity based on demand signals such as queue depth, active sequences, token rate and GPU saturation.
Scale-out triggers on queue wait and active sequence pressure instead of waiting for p95 latency to breach.
Which leading indicator should cause scale-out before users feel the incident?
REMEMBERScale on the constraint that predicts failure.
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