The Definitive Guide toAI Data Centers
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Guide › Part 9

Part 9

Storage & Data

9 chapters

9.1
Storage in the AI Lifecycle: Why It Determines GPU Efficiency
Storage decides whether an accelerator computes or idles, and getting it right means designing for four competing I/O personalities that can exhaust different resources on the same flash and fabric.
9.2
Parallel & Distributed File Systems
A parallel file system keeps a GPU saturated only if its metadata plane survives the workload’s small-file storm and its data plane supplies the bytes; test billions of small files when the corpus contains them, because sequential bandwidth alone cannot qualify that purchase.
9.3
NVMe Tiers, GPUDirect Storage & the CPU-Bypass Data Path
Whether a GPU stays fed can turn on per-request latency before aggregate bandwidth; a supported direct path routes bytes from flash straight into HBM without a host-memory bounce buffer, but the measured workload and surviving path decide whether that bypass earns its cost.
9.4
Checkpointing for Large-Scale Training
Checkpointing is a training cluster's goodput control knob — the interval, tier, and write bandwidth you choose decide how many GPU-hours each failure erases, and at frontier scale failures arrive constantly.
9.5
Data Ingestion, Preprocessing & the Data-Loader Path
The data loader sits in the critical loop of every training step; get its format, sharding, and CPU budget wrong and the GPUs stall, burning depreciation while they wait to be fed.
9.6
Object Storage, Data Lakes & the Capacity Tier
Object storage holds the whole corpus, every checkpoint lineage, and every shipped model; its cost turns less on vendor choice than on whether you build it as a flash-fronted serving layer the GPUs read from.
9.7
Inference & KV-Cache Storage: The New Memory Hierarchy
Inference made the KV-cache a first-class storage problem: its bytes now spill past HBM, and where you let them land — DRAM, CXL, NVMe, or Ethernet-flash — sets your tokens-per-second and cost-per-token.
9.8
Sizing, Data Gravity & Resilience
Size the hot tier by per-GPU bandwidth when sequential delivery binds, check metadata, retained bytes and endurance, place the corpus where the compute can reach it, and control checkpoint incast — miss a budget in the surviving system and idle accelerators absorb the cost.
9.9
The Data-Prep Supercomputer: Offline Data Processing
Before any GPU sees a token, a second cluster must dedupe, filter, decontaminate, and tokenize trillions of tokens — undersize it and you starve the training fleet or waste GPU-hours on string processing.