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Design the checkpoint store for a lab running several large training jobs: write bursts, retention, resharding and lineage.

Every 30 minutes a thousand GPUs write a terabyte in a burst that must finish in a minute, then nothing until the next. The burst arithmetic that sizes the write tier, the two-stage path to durable storage, retention that keeps the right checkpoints, resharding so a 512-GPU checkpoint resumes on 256, and lineage.

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.

Every 30 minutes a thousand GPUs write a terabyte in a burst that must finish in a minute, then nothing until the next. The burst arithmetic that sizes the write tier, the two-stage path to durable storage, retention that keeps the right checkpoints, resharding so a 512-GPU checkpoint resumes on 256, and lineage.

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The concepts behind this question

Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.

Core
🔌 Networking & StorageSign in
Parallel Filesystems vs Object StorageA training cluster's storage has two very different jobs: stream terabytes of training data to thousands of GPUs at a steady rate, and absorb a multi-terabyte checkpoint burst every few minutes. Parallel filesystems (Lustre, GPFS, WEKA, VAST, FSx) give POSIX semantics and hundreds of GB/s of aggregate throughput; object storage (S3 and its equivalents) gives durability and cost at a fraction of the price with high first-byte latency. Almost every real cluster uses both, and the interview question is which job goes where and how big each tier has to be.
Foundational
🔌 Networking & Storage
Dataset Lifecycle: Ingest, Shard and RetainA training dataset is not a file, it is a pipeline with four stages and a retention policy, and each stage has a different bottleneck. Ingest is metadata-bound rather than bandwidth-bound. Tokenization is CPU work that should happen once offline rather than every epoch. Sharding decides whether the training read is a stream or a storm of small files. And retention decides how much of the bill is paid for bytes nobody reads.
Advanced
📐 AI Systems Design🔒 Premium
Evaluation and Data Pipeline InfrastructureBehind every model release is a pipeline that turns raw text into training shards and a harness that runs thousands of evaluation prompts against every checkpoint, and both are infrastructure problems with GPU-sized budgets. The data side is a batch system: dedup, filter, tokenize and shard petabytes with lineage. The eval side is a serving system in disguise: run a benchmark suite against a checkpoint in minutes, on shared GPUs, reproducibly, with results a researcher can trust. This page designs both, derives the compute and storage they need, and gives the reproducibility rules that separate a real harness from a script.
Foundational
🖧 Hardware & Cluster Build-Out
The Bill of Materials for a Training ClusterA GPU cluster is not a pile of GPUs. A 512-GPU scalable unit built to NVIDIA's DGX SuperPOD B300 reference architecture needs 64 nodes, four separate networks, thousands of transceivers, storage that can absorb a checkpoint burst, a management plane, racks, power distribution and cooling equipment. Writing the list out in order is how a design becomes a purchase order, and the items people forget are the ones that hold up a deployment for weeks.
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FEDITOR'S NOTE

Scored on burst arithmetic (bytes per checkpoint, write window, bandwidth), on the local-then-object two-stage path with async upload, on a retention policy that keeps milestone checkpoints, and on a sharded format with metadata that reshards without a full rewrite.

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