Cold starts and fast model loading
Break a GPU replica's start-up into its stages (node, image, weights, runtime, warm-up), compute load times from storage and network throughput, then cut each one: pre-baked images, node-local caches, parallel and streamed weight loading, spare capacity, compile and graph caches, readiness design and when scale-to-zero is worth it.
Every autoscaling decision pays a delay tax: the time between "we need another replica" and "that replica serves its first request". For GPU inference that delay is routinely three to ten minutes, and as the previous lesson showed, it sets how much headroom you must buy. Shrinking cold start is therefore one of the most valuable optimisations in the whole stack: every minute you remove returns hardware.
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