Samsung DDR5 128GB 4Rx4 LRDIMM 4800MHz Server Memory

The Samsung DDR5 128GB 4Rx4 LRDIMM represents a breakthrough in server memory density, engineered for next-generation data center architectures requiring unprecedented memory footprints. Built on DDR5 technology with load-reduced DIMM (LRDIMM) architecture, this module consolidates 128GB per slot, enabling single-socket systems to achieve multi-terabyte memory configurations without requiring multi-socket server complexity. The 4Rx4 design (4 ranks, 4 banks per rank) optimizes signal integrity and thermal efficiency across high-density populated memory channels, critical for systems operating at sustained full capacity.

Launched in 2023-Q2, this module targets demanding workloads including in-memory database platforms (SAP HANA, Oracle Database with Exadata), real-time analytics engines, and large-language model inference hosting. The 4800MHz speed grade balances bandwidth delivery with power efficiency, providing 38.4GB/s per-channel throughput while maintaining compatibility with leading Xeon and EPYC processor architectures. Load reduction circuitry minimizes electrical strain on memory controllers, enabling reliable operation in densely-populated memory subsystems spanning 8+ DIMM configurations.

The module's 128GB capacity directly addresses the memory bottleneck for organizations deploying distributed training frameworks, vector databases, and graph analytics platforms where per-server memory locality reduces network fabric utilization. Thermal-optimized design with standard heatspreader reduces hotspot formation in high-density server chassis, supporting continuous operation in virtualized environments where memory overcommit is performance-critical.

Ideal for

• SAP HANA enterprise resource planning (ERP) deployments requiring 2TB+ per-server main memory for consolidated multi-tenant production instances
• Oracle Database in-memory column store (IMCS) configurations hosting real-time financial analytics and order management systems
• Large language model (LLM) inference serving where model weights and attention cache reside in server memory to eliminate disk I/O latency
• Genomic sequencing and life sciences research platforms requiring terabyte-scale genomic datasets in addressable memory for sub-millisecond query response
• Real-time fraud detection and anti-money laundering (AML) engines operating on streaming transaction datasets exceeding 500GB in active working set
• Distributed machine learning training frameworks (PyTorch, TensorFlow) with gradient accumulation across GPU clusters requiring host memory buffers up to 2TB

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.