Collection: Storage & Memory

Feed the Model. Sustain the Workload.

There's a hierarchy to AI infrastructure performance, and storage sits at its foundation. A GPU cluster without adequate storage throughput is like a high-performance engine running on a clogged fuel line — the potential is there, but the system can't reach it. Dataset loading, checkpoint writing, model weight serving, feature store access, log aggregation — every one of these operations touches storage, and every one of them has the potential to become a bottleneck that limits what your compute investment can actually deliver.

Memory tells a similar story. The gap between GPU VRAM and system DRAM is where many AI deployments quietly lose performance — in the data transfers, the cache misses, the memory bandwidth constraints that force models to run at lower batch sizes than the hardware should theoretically support. Getting the memory architecture right means understanding not just capacity, but bandwidth, latency, and the specific access patterns of your AI workloads.

DVUN's Storage & Memory collection addresses both layers of this challenge. From ultra-fast NVMe drives for local dataset staging to purpose-built AI storage nodes for shared cluster storage, from high-bandwidth DDR5 memory kits to cache expansion solutions that extend effective memory capacity — this collection is built for teams who understand that storage and memory are not supporting characters in the AI infrastructure story. They're lead roles.

Four Categories, One Mission

• NVMe Storage: PCIe Gen4 and Gen5 NVMe drives for local dataset staging, checkpoint caching, and model weight serving. The fastest local storage available for AI workloads.
• AI Storage Nodes: Purpose-built shared storage platforms for AI clusters — all-NVMe and hybrid configurations with the throughput to feed multiple GPU nodes simultaneously.
• Memory Kits: DDR5 ECC registered DIMMs in high-capacity configurations for AI servers that need to hold large datasets, feature stores, or model weights in system memory.
• Cache / Expansion: CXL memory expansion, storage cache accelerators, and persistent memory solutions that extend effective memory capacity beyond DRAM limits.

Storage Architecture for AI: What Actually Matters

Sequential read throughput determines how fast you can load training data. Random read IOPS determines how efficiently you can serve model weights for inference. Write throughput determines how quickly you can save checkpoints during long training runs. And memory bandwidth determines how fast your CPU can move data between storage, system memory, and the GPU. DVUN's storage and memory products are specified with all of these metrics in mind — not just the headline numbers that look good on a spec sheet.

Performance Benchmarks to Expect

• NVMe sequential read: up to 14GB/s per drive on Gen5 platforms
• AI storage node aggregate throughput: up to 200GB/s on all-NVMe configurations
• DDR5 memory bandwidth: up to 89.6 GB/s per channel on high-speed kits
• CXL memory expansion: up to 512GB additional capacity per expansion module

Storage That Keeps Pace with Your Ambitions

The teams that build the best AI infrastructure are the ones who think about storage and memory as seriously as they think about compute. DVUN's Storage & Memory collection gives you the hardware to build a data layer that never becomes the reason your training job is slow or your inference latency is high. Request a quote for complete storage architecture designs, or browse the subcategories to find the specific components your build requires.