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What storage throughput does a 16k-GPU training cluster need?

The token stream is tens of megabytes a second and the checkpoints are tens of gigabytes a second; the two workloads differ by a thousand and want different tiers. The chain from tokens per second to bytes, the checkpoint burst, and the restore that sets the real spec.

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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The concepts behind this question

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Advanced

🔌 Networking & Storage🔒 Premium

Checkpoint I/OA checkpoint of a 70B model's training state is 1.13 TB, and a 405B model's is 6.5 TB. Written synchronously every half hour, it stalls thousands of GPUs for as long as the slowest rank takes to reach the disk. The design that scales writes each rank's shard in parallel, copies it off the GPU to host memory first so the run resumes in seconds, and drains it to durable storage in the background. The arithmetic is bytes per rank against the bandwidth of each hop, and the goal is a checkpoint that costs the run under 1% of its time.

Advanced

🔌 Networking & Storage🔒 Premium

Data Loading Pipelines for TrainingThe dataloader is the only part of a training job that runs on the CPU, the disk and the network at once, and it is the part most often found starving the GPUs. A pipeline that keeps 1,024 accelerators fed has to read sharded files sequentially, decode and tokenize in parallel workers, prefetch several batches ahead, pin memory for the PCIe copy, and do it deterministically enough to resume mid-epoch. The symptom of failure is a GPU at 30% utilization with nothing wrong on the GPU.

Core

🧮 Napkin Math & CapacitySign in

GPU-Hours and Time to TrainThe fleet equation turns a training run's FLOPs into a schedule: time = 6ND divided by (GPUs times peak FLOPS times MFU). Every term is a stated assumption, and the interviewer grades the assumptions rather than the digits: which peak, which MFU, and what happens to the answer when MFU falls from 40% to 30%. This page works three runs end to end (an 8B, a 70B and a 405B), inverts the equation for the GPU count a deadline needs, and shows the sensitivity that separates a considered estimate from a lucky one.

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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Next in this trackTraffic peaks at three times the daily average. Capacity-plan the serving fleet.Next in this trackWhat does one training token cost?Next in this trackEstimate the latency of one decode step for a 70B model under tensor parallelism across eight H100s

FEDITOR'S NOTE

The interviewer is checking that the candidate computes the data-read rate and discovers it is small, then finds the real requirement in checkpoint write and restore. A candidate who sizes the filesystem on the dataset has the wrong workload.

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