Checkpoints and datasets both live on storage. Why does one system sized for both usually serve neither well?

Can one storage system serve datasets and checkpoints? Follow a save from staging to recovery, calculate its budget beside protected reads, and learn when separate paths actually solve the contention.

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

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The concepts behind this question

Use these concept explanations to review the ideas behind this question.

Foundational

🖧 Hardware & Cluster Build-Out

Cluster Bring-Up: Firmware, Drivers and the StackBring-up validates the dependencies between firmware, drivers, networking and the runtime. Checking each layer makes faults easier to locate.

Advanced

🚀 Inference & Serving🔒 Premium

Reasoning Budgets and Answer LatencyReasoning tokens consume generation time and retained state before an answer becomes usable. Separate the first generated token from the first answer token, reserve output capacity, and evaluate reasoning limits against task success.

Advanced

🚀 Inference & Serving🔒 Premium

Reranking and Retrieval BudgetsA reranker spends extra computation on retrieved candidates. Count query-document pairs, protect permission boundaries and measure whether better evidence justifies its added queueing and latency.

Foundational

🧮 Napkin Math & Capacity

KV Cache SizingDerive cache payload from the stored attention state, then account for mixed sequence lengths, block allocation and per-device limits. Worked examples distinguish exact bytes from deployable capacity.

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FEDITOR'S NOTE

Protect dataset reads while calculating the checkpoint publication deadline. Track the age of recoverable state separately from the pause and the upload queue.

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