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mediumDatabricksHugging Face

Design the registry and deploy pipeline for model weights: 141 GB artifacts, versioning, distribution to a thousand nodes, integrity.

Bind weights, tokenizer, configuration and evaluation to one immutable release. Follow verification, prefetch, canary readiness and rollback, with bandwidth floors that do not pretend to be rollout measurements.

Updated Sep 2026 · Learn how AI infrastructure works through explanations, worked examples and diagrams, then practise applying it to interview questions.

Bind weights, tokenizer, configuration and evaluation to one immutable release. Follow verification, prefetch, canary readiness and rollback, with bandwidth floors that do not pretend to be rollout measurements.

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The concepts behind this question

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🖧 Hardware & Cluster Build-Out

The Bill of Materials for a Training ClusterA cluster bill of materials includes networking, storage, management, power, cooling and spares as well as GPUs. Size each from the workload and topology.

📐 AI Systems Design

The AI Infra Design Round PlaybookA design interview is a chance to explain requirements, capacity and tradeoffs clearly. Use a flexible structure that leaves time for mechanisms, failures and follow-up questions.

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GPU Job Scheduler DesignA GPU scheduler decides which jobs run, where they run and when resources can be reclaimed. Its placement rules must account for whole jobs, topology and tenant policy.

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Multi-Tenant Fine-Tuning ServiceA fine-tuning service turns a tenant’s dataset into an evaluated model or adapter. Its pipeline must protect data while scheduling training and serving the result efficiently.

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UP NEXT ON YOUR JOURNEY

Next in this trackDesign rate limiting for an LLM API. Why tokens instead of requests, and how does a bucket work when the cost is unknown until the end?Next in this trackDesign a multi-region inference deployment: capacity per region, routing, failover, and getting the weights everywhere.Next in this trackDesign the playground behind an LLM API: streaming, sessions, quotas and abuse, for a million free-tier users.

FEDITOR'S NOTE

Scored on explicit capacity assumptions, correct state and identity boundaries, failure handling and a justified design choice.

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