Storage
AI storage systems are high-performance parallel file systems and NVMe storage tiers engineered to feed GPU clusters at 50–200+ GB/s throughput — ensuring GPUs are never starved of data during training or inference.
Traditional enterprise storage (NAS, SAN) is designed for transactional workloads with moderate throughput and high IOPS. AI workloads are fundamentally different: they require sequential reads of massive files (training datasets), checkpoint writes of multi-terabyte model states, and concurrent access from hundreds of GPUs.
A standard NFS server serving 1,000 GPUs would create an immediate bottleneck, delivering perhaps 2 GB/s shared across all GPUs — resulting in 2 MB/s per GPU and 95%+ GPU idle time. AI storage requires parallel file systems that stripe data across dozens or hundreds of storage servers, delivering aggregate throughput proportional to cluster size.
The leading AI storage technologies are: Lustre (open-source, used by most supercomputers), IBM GPFS/Spectrum Scale (enterprise-grade, widely used in HPC), WEKA (software-defined, cloud-native), VAST Data (NVMe-based, simplified management), and GPU-direct storage via RDMA.
These systems achieve high throughput by distributing data across multiple storage nodes and reading in parallel. A 20-node WEKA cluster can deliver 400+ GB/s throughput — sufficient for a 500-GPU training cluster. Storage is typically tiered: NVMe for active training data, SSD for checkpoint storage, and HDD or object storage for archival.
AI storage represents 8–15% of total cluster capital cost. For a USD 60 million GPU cluster, storage investment is typically USD 5–9 million. While significant, inadequate storage can reduce cluster utilisation by 50%+ — making it the highest-leverage infrastructure component. Investing in purpose-built storage returns multiples in improved GPU utilisation and training efficiency.
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