If you're running dedicated LLM inference deployments, this explains why naive GPU-utilization-based autoscaling backfires under bursty load and gives concrete guidance on better scaling signals and cold-start budgeting to avoid latency spikes.
An article on the pitfalls of autoscaling LLM inference workloads, explaining why GPU utilization is a misleading scaling signal when request queues back up and new replicas take minutes to warm up.
It covers choosing better autoscaling metrics, tuning scale-up and scale-down windows, and budgeting for cold-start latency on dedicated inference deployments.
Transcript
GPU utilization can read healthy while your queue backs up, and a new replica takes minutes to warm. Here's how to pick autoscaling metrics, tune scale-up/down windows, and budget for cold starts on dedicated inference.
