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Design autoscaling for an LLM service whose traffic triples within a minute. What signal, what cold-start budget, what warm pool?

A replica takes 70 seconds to become useful and the burst arrives in 60, so the autoscaler cannot win the first minute; the warm pool has to. The leading signal, the cooldowns derived from the cold start, the warm-pool size from Little's law and the burst profile, and the cost of every second of cold start left in.

Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.

A replica takes 70 seconds to become useful and the burst arrives in 60, so the autoscaler cannot win the first minute; the warm pool has to. The leading signal, the cooldowns derived from the cold start, the warm-pool size from Little's law and the burst profile, and the cost of every second of cold start left in.

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The concepts behind this question

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🚀 Inference & Serving🔒 Premium
Inference Autoscaling and Cold StartsScaling an LLM fleet is harder than scaling a web service because a replica takes minutes to become useful (pull an image, load 141 GB of weights, warm the cache) and costs several dollars an hour while idle. The signals that work are queue depth and TTFT against the SLO, not GPU utilization, which is misleading for memory-bound decode. The design is a warm pool sized for the burst, hysteresis so the fleet does not thrash, and a cold-start path measured in seconds through snapshots and weight streaming.
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📐 AI Systems Design🔒 Premium
Serverless GPU PlatformsA serverless GPU platform lets a customer deploy a function or a model and pay only while it runs, so the platform has to start a GPU workload in seconds, pack many customers onto shared hardware without letting them see each other, and keep enough capacity warm that a burst does not wait for a cold start. Each is a design problem with numbers: the cold-start chain and the snapshot that shortens it, bin-packing memory-sized workloads onto fixed-size GPUs, the isolation boundary and its cost, and the economics of idle capacity against cold starts. This page designs the platform and derives the trade-offs.
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📐 AI Systems DesignSign in
Designing for Latency SLOsA latency objective is met or missed by the sum of a chain of delays, and the way to design for it is to write the chain down with a number on every link, find the links that dominate at the tail, and attack those. For an LLM request the chain is network, gateway, router, queue, prefill, then the decode loop, and the tail is shaped by queueing and by the size of the batch the request lands in. This page decomposes a 500 ms time-to-first-token budget link by link, derives how queueing turns a comfortable median into a broken p99, and gives the design moves (admission control, chunked prefill, priority lanes, hedging) that hold it.
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🧮 Napkin Math & Capacity🔒 Premium
Capacity Planning and UtilizationCapacity planning for GPUs is deciding how many to have next quarter given that they cost money whether busy or not, that demand arrives in bursts, and that a queue near saturation produces waits that grow without bound. This page works the planning arithmetic for a serving fleet (peak demand, headroom, the p99 penalty of running hot) and a training platform (job mix, queue wait, the value of a shared pool), and gives the queueing intuition that makes 70% look full. The number that decides everything is utilization, and it has a ceiling set by latency, not by hardware.
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FEDITOR'S NOTE

Scored on deriving the warm pool from the burst profile and the cold start, on a leading signal with cooldowns longer than the cold start, on shrinking the cold start as the change with the largest effect, and on the cost trade stated as a number.

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