AI Storage Nodes

Shared Storage Built for the Demands of AI Clusters

When you move from a single GPU server to a multi-node training cluster, a new infrastructure challenge emerges: how do all your compute nodes access the same training data, the same model checkpoints, and the same output artifacts — simultaneously, at the speed the GPUs demand? The answer is a purpose-built AI storage node: a dedicated storage platform designed from the ground up for the specific I/O patterns, throughput requirements, and concurrency demands of distributed AI workloads.

General-purpose NAS systems weren't designed for this. They were designed for file sharing, backup, and enterprise document workflows — workloads with very different I/O profiles than AI training. An AI storage node needs to sustain hundreds of gigabytes per second of aggregate read throughput across dozens of concurrent client connections, with the low latency and consistent performance that keeps GPU utilization high across the entire cluster. That's a fundamentally different engineering challenge, and it requires fundamentally different hardware.

DVUN's AI storage node collection is built around this understanding. Every platform we stock has been evaluated for its aggregate throughput, client concurrency, protocol support, and compatibility with the distributed file systems and object storage systems that AI teams actually use.

Core Capabilities

  • All-NVMe Architecture: No spinning disk, no hybrid tiers — pure NVMe flash for consistent, predictable throughput that doesn't degrade under mixed workloads.
  • High-Speed Network Connectivity: 100GbE to 400GbE network interfaces to match the bandwidth of your cluster's switching fabric and prevent the storage node from becoming a network bottleneck.
  • Distributed File System Compatibility: Validated for use with Lustre, GPFS/Spectrum Scale, BeeGFS, WekaFS, and S3-compatible object storage protocols.
  • Scale-Out Architecture: Add storage nodes to increase both capacity and throughput linearly — no single-node bottleneck as your cluster grows.
  • Data Protection Options: Erasure coding and replication configurations to match your durability requirements without sacrificing performance.
  • GPU-Direct Storage Support: Select platforms support NVIDIA GPUDirect Storage, enabling direct DMA transfers between storage and GPU memory for maximum training throughput.

Sizing Your Storage Node for Your Cluster

For a 4–8 GPU Node Cluster: A single all-NVMe storage node with 100GbE connectivity and 50–80GB/s aggregate throughput is typically sufficient to keep all compute nodes fed during training. Our entry-level AI storage platforms are designed for exactly this scale — high performance without the complexity of a distributed storage cluster. Pair with our NVMe Storage drives for local staging capacity on each compute node.

For a 16–100+ GPU Node Cluster: A scale-out storage cluster with multiple nodes, 400GbE connectivity, and aggregate throughput in the 100–200GB/s range is required to prevent storage from limiting training efficiency. Our high-end AI storage platforms support scale-out configurations and integrate with enterprise distributed file systems. See our Networking collection for the high-speed fabric required to connect storage nodes to your compute cluster.

Performance Specifications

  • Aggregate sequential read: 50GB/s to 200GB/s per node
  • Raw NVMe capacity: 100TB to 1PB+ per node depending on configuration
  • Network interfaces: 2x 100GbE to 4x 400GbE per node
  • Client concurrency: 32 to 256 simultaneous client connections
  • Protocol support: NFS v4.1, SMB 3.0, S3, Lustre, BeeGFS client
  • Data protection: RAID 5/6, erasure coding (4+2, 8+2), replication
  • Form factor: 2U to 4U rack-mount

Your Cluster's Data Layer, Done Right

A GPU cluster without adequate shared storage is a collection of isolated compute nodes, not a coherent AI infrastructure. DVUN's AI storage node collection gives you the shared data layer that transforms individual servers into a coordinated training and inference platform. Request a quote for cluster-scale storage designs, or contact our team for a throughput analysis based on your specific training workload and cluster size.

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