Feeding the GPUs
AI training reads enormous datasets over and over, in parallel, across many GPUs. The bottleneck is rarely capacity — it is sustained, concurrent throughput and low metadata latency over thousands of small files. Underfeed the GPUs and you waste the most expensive hardware in the building; analytics platforms (Spark, data lakes) have the same hunger for parallel reads.
Two storage shapes win here: a dedicated scale-out file/object platform built for massive parallel throughput (FlashBlade), and high-end all-flash arrays (NetApp AFF A-Series, Hitachi VSP One) that pair extreme IOPS with the data services enterprises still need. The choice hinges on dataset size, how many GPUs you are feeding, and whether the same data must also serve transactional apps.
Indicative best-fit guide for this workload — Servnet confirms the final design and pricing on quote.
What we’d put in front of you
NetApp AFF A90
GPU-classHigh-end all-NVMe AFF built for AI data pipelines — extreme throughput with ONTAP data management; certified for GPU/AI reference architectures.
Explore NetApp AFF A90 →Pure FlashBlade
Parallel I/OScale-out file + object engineered for massively parallel reads — a favourite for keeping large GPU clusters and analytics pipelines saturated.
Explore Pure FlashBlade →Hitachi VSP One
Enterprise AIAll-NVMe block (to ~50M IOPS high-end) with enterprise data services — when AI sits alongside mission-critical apps on one resilient platform.
Explore Hitachi VSP One →Indicative positioning for this workload — not a benchmark. We compare to your exact requirement.
Key decisions for ai & analytics
AI & Analytics storage — FAQs
What storage keeps GPUs from sitting idle?
Storage that delivers sustained, massively parallel throughput with low metadata latency. Dedicated scale-out platforms (Pure FlashBlade) and high-end all-flash (NetApp AFF A90) are built for this — they feed many GPUs reading the same datasets concurrently. Size by required GBps and the number of parallel clients, not just capacity.
Do I need a dedicated AI storage platform?
For large, throughput-critical training estates, a dedicated scale-out platform (FlashBlade) usually wins. For smaller AI projects, or where AI runs alongside enterprise apps, a high-end all-flash array (AFF A90, Hitachi VSP One) with strong data services can serve both. We size to your GPU count, dataset size and growth.
Does capacity or performance matter more for AI?
Performance — specifically parallel throughput and metadata speed — is almost always the constraint, because GPUs stall waiting for data. Capacity matters for the dataset, but the platform must deliver that capacity at high concurrency. Our Solution Finder lets you weigh both for an indicative recommendation.
Size it for your ai & analytics workload
Set your capacity, performance and protection needs in the Storage Solution Finder for an instant recommendation — or talk to our team for an impartial, vendor-neutral design and quote.