High-Throughput Storage Arrays: Specifications and Use Cases for AI Teams

The enterprise storage array market has bifurcated in the AI era: legacy general-purpose arrays designed for enterprise application workloads, and purpose-engineered high-throughput arrays built to satisfy the I/O demands of GPU clusters. The performance gap between these categories is substantial. A conventional all-flash array might deliver 10 to 20 GB/s of aggregate throughput; a purpose-built AI storage array from vendors such as VAST Data, NVIDIA's DGX Storage, DDN, or NetApp delivers 100 to 500 GB/s from a single system. For AI teams evaluating storage infrastructure, knowing which specifications to focus on separates meaningful differentiation from marketing noise.

Specifications That Define High-Throughput Storage Arrays

Aggregate sequential read bandwidth is the headline specification — the maximum sustained data throughput the array can deliver across all client connections simultaneously. Verify this number with workloads that match your access pattern (large sequential reads, not small random IOPS). Metadata throughput in operations per second determines performance for small-file datasets — an array with excellent sequential bandwidth may have a metadata bottleneck that cripples training on ImageNet-style datasets. Client concurrency — the number of simultaneous connected clients — determines whether the array can serve an entire GPU cluster without degrading per-client throughput. Front-end network bandwidth (the total bandwidth of all host-facing ports) must match or exceed the aggregate throughput specification; mismatches here are a common source of underperformance.

Storage Array Architectures for AI

Scale-up arrays add additional drive shelves to a fixed controller pair, increasing capacity and throughput to a controller-defined ceiling. They are straightforward to manage but create a single point of architectural constraint. Scale-out arrays add additional nodes, each with its own compute, networking, and drive capacity, allowing both throughput and capacity to grow linearly with node count. VAST Data's disaggregated shared-everything architecture uses NVMe drives shared across a cluster of stateless controller nodes via RDMA, achieving very high throughput with no theoretical scaling ceiling. DDN's EXAScaler runs Lustre on dense NVMe nodes, familiar to HPC teams. The right architecture depends on your current and projected workload scale.

• Aggregate sequential read bandwidth: minimum requirement for your GPU cluster's sustained data consumption rate with 25 percent headroom.
• Metadata OPS: critical for small-file datasets; request benchmark data at your dataset's average file size and access concurrency.
• Front-end network ports: verify total port bandwidth equals or exceeds stated aggregate throughput.
• Failure domain isolation: understand how a single node or controller failure affects available throughput.
• Data services overhead: inline compression, deduplication, and encryption all consume compute cycles; quantify their throughput impact.
• Scalability ceiling: determine how many nodes or shelves the architecture supports and whether that ceiling accommodates your 3-year growth projection.

Use Case Mapping: Which Array Architecture Fits Your AI Workload

Small GPU clusters (8 to 32 GPUs) running vision or NLP training on relatively modest datasets are well-served by a single high-performance all-flash array with NFS or direct-attached NVMe — the operational simplicity justifies a modest throughput headroom investment. Mid-scale clusters (32 to 256 GPUs) training large models benefit from a scale-out NVMe-oF or parallel file system array capable of 50 to 200 GB/s. Large-scale clusters (256 GPUs and beyond) training foundation models require the highest-tier purpose-built systems — VAST Data, DDN EXAScaler, or WekaFS — delivering hundreds of GB/s with distributed metadata to eliminate hot spots.

Benchmark your shortlisted arrays with your actual workload I/O pattern before purchase. Published specifications are measured under favorable conditions that may not match your production access profile.

How Nexus Compute Helps

Nexus Compute sources high-throughput storage arrays from the leading purpose-built AI storage vendors and integrates them with GPU compute infrastructure and high-speed network fabric. Our team performs detailed workload analysis to match the right storage architecture to your cluster scale and dataset access patterns. We manage procurement, integration, and validation — and maintain relationships with storage vendors that give our customers access to the latest platforms ahead of general availability.