Collection: Memory Kits

Memory Is Not a Commodity. Treat It Like Infrastructure.

In the procurement process for AI servers, memory is often the last line item — the component that gets spec'd to the minimum required and ordered from whoever has it cheapest. This is a mistake that shows up in production in ways that are difficult to diagnose: intermittent ECC errors that corrupt training runs, memory bandwidth limitations that force lower batch sizes, capacity constraints that require constant dataset chunking, and compatibility issues that cause system instability under sustained load.

DVUN's Memory Kits collection is built on a different philosophy. We treat server memory as a critical infrastructure component — one that deserves the same attention to specification, compatibility, and quality as the CPU or GPU it's paired with. Every memory kit in our catalog is validated for compatibility with the server platforms it's designed for, rated for the sustained workloads of AI training and inference environments, and sourced from manufacturers with the quality control processes that enterprise deployments require.

What to Look for in AI Server Memory

• ECC (Error Correcting Code): Non-negotiable for AI servers. ECC memory detects and corrects single-bit errors in real time, preventing the silent data corruption that can invalidate training runs or cause inference errors.
• Registered (RDIMM) vs. Load-Reduced (LRDIMM): RDIMMs are the standard for most AI server configurations. LRDIMMs support higher capacity per channel and are required for maximum memory population on high-core-count platforms.
• DDR5 Speed Grades: DDR5-4800 to DDR5-6400 speed grades — higher speeds increase memory bandwidth, which directly impacts AI workload performance on CPU-bound preprocessing and inference tasks.
• Capacity per DIMM: 32GB, 64GB, 96GB, and 128GB DIMM capacities to reach the total system memory your workload requires within your platform's DIMM slot count.
• Thermal Specifications: Memory that maintains rated performance under the sustained thermal load of a 24/7 AI server environment, not just short-burst benchmarks.
• Platform Validation: Every kit in our catalog includes a compatibility matrix showing which server platforms and CPU generations it has been validated for.

Capacity Planning for AI Workloads

For Training Servers: The general rule is to provision system memory at 2–4x the total GPU VRAM in the system. A server with 8x 80GB GPUs (640GB total VRAM) should have 1.28TB to 2.56TB of system DRAM for efficient data pipeline operation. Our 128GB LRDIMM kits make reaching these capacities practical on dual-socket platforms. Pair with our GPU Servers for complete training node builds.

For Inference Servers: Large language model inference increasingly requires holding model weights in system memory for fast loading and multi-model serving. A 70B parameter model in FP16 requires approximately 140GB of memory just for weights. Our high-capacity memory kits make it practical to hold multiple models in memory simultaneously for low-latency serving. See our Cache / Expansion for CXL memory expansion options that extend capacity beyond DRAM limits.

Memory Kit Specifications

• Type: DDR5 ECC Registered (RDIMM), Load-Reduced (LRDIMM)
• Speed grades: DDR5-4800, DDR5-5600, DDR5-6400
• Capacities per DIMM: 32GB, 64GB, 96GB, 128GB
• Kit configurations: single DIMM to 32-DIMM full-population kits
• Maximum system capacity: up to 6TB on select dual-socket platforms
• Voltage: 1.1V standard, 1.05V low-voltage on select models
• Operating temperature: 0–85°C

Spec It Right. Run It Reliably.

The right memory kit is the one that's compatible with your platform, sized for your workload, and built to run reliably under the sustained demands of AI infrastructure. DVUN's Memory Kits collection gives you exactly that — with the compatibility documentation and sourcing expertise to make sure you get it right the first time. Request a quote for full server memory population orders.