Autoscaling GPU Inference for Llama Services (EXPLAINED)
TL;DR — Quick Answer
HPA on custom metrics (queue depth, GPU util), scale-to-zero trade-offs with cold start, model server sidecars, multi-model bins, cluster autoscaler for GPU nodes, and cost caps — vLLM continuous batching helps efficiency.
The Interview Question
How do you autoscale Llama inference on Kubernetes under variable load?
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