Design a platform that serves twenty open-weights models of varying size to internal teams.

Twenty models of different sizes is a packing problem with a cold-start penalty, and the two pull in opposite directions. What stays resident, what loads on demand, the arithmetic that decides which is which, and the interface that stops every team from asking for a dedicated replica.

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.

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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.

Foundational

📐 AI Systems Design

Multi-Region Serving and FailoverRunning inference in more than one region buys latency for distant users and survival when a region fails, and it costs a second fleet that must be capable of absorbing the first one's traffic. The design turns on three decisions: whether regions are active-active or active-passive, what state has to cross regions and what deliberately does not, and how much headroom each region carries so a failover does not simply move the outage.

Foundational

🗂️ Scheduling & Orchestration

Slurm vs KubernetesEvery GPU platform team has this argument, and the two schedulers were built for different jobs: Slurm for long, large, all-or-nothing training on bare metal; Kubernetes for many services that scale up and down. Training fleets run Slurm because gang scheduling, topology and MPI-style launch are native there; serving fleets run Kubernetes because autoscaling and rolling deploys are native there. A platform that does both picks a hybrid: Slurm on Kubernetes (Slinky, Soperator) or a batch scheduler on Kubernetes (Kueue, Volcano, KAI). The interview question is which, for which workload, and why.

Foundational

🧮 Open Weights & Serving Engines

Capacity Planning for Open-Weights FleetsPlanning a fleet for a sparse open-weights model works differently from planning one for a dense model, because memory follows total parameters and throughput follows active parameters, and those now differ by more than twenty times. The sizing goes in one direction only: from a traffic forecast to tokens per second, to replicas at a measured operating point, to GPUs, to racks and kilowatts. Doing it in the other direction, from an available GPU count, produces a fleet that fits the hardware rather than the demand.

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UP NEXT ON YOUR JOURNEY

Next in this trackAn SGLang deployment underperforms. Tune it against the project's own published targets.Next in this trackYou upgraded the serving engine and throughput fell 15 percent. Find the cause.Next in this trackA model interleaves linear-attention and full-attention layers. What changes about serving it?

FEDITOR'S NOTE

Scored on the resident-versus-on-demand decision from request rate and load time, on cold start as the dominant cost, and on the interface that shapes demand.

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