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Lenovo 5M30V06900 64GB DDR4-3200MHz RDIMM 2Rx4 CL22 Memory

Lenovo 5M30V06900 64GB DDR4-3200MHz RDIMM 2Rx4 CL22 Memory

SKU:5M30V06900
  • 64GB DDR4 Memory Module
  • Speed: 3200MHz
  • Type: RDIMM (Registered DIMM)
  • Rank: 2Rx4 (Dual Rank, x4 organization)
  • Latency: CL22
  • Designed for Lenovo systems
  • Enhances system performance and multitasking capabilities
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Product Overview

The Lenovo 5M30V06900 is a 64GB DDR4-3200MHz RDIMM memory module. It features a 2Rx4 rank configuration and CL22 latency, designed to enhance the performance of compatible Lenovo systems.

Technical Information

Capacity64GB
Memory TypeDDR4
Speed3200MHz
Form FactorRDIMM

Additional Specifications

Rank2Rx4
CAS LatencyCL22
ManufacturerLenovo

Product Description

The Lenovo 5M30V06900 is a high-capacity 64GB DDR4 memory module engineered to boost the performance of compatible Lenovo servers and workstations. Operating at a speed of 3200MHz, this module provides ample bandwidth for demanding applications and multitasking environments. As a Registered DIMM (RDIMM), it incorporates a register chip that buffers command and address signals, reducing electrical load on the memory controller and allowing for higher memory capacities and improved stability in systems with multiple memory modules. The module's 2Rx4 configuration signifies that it is dual-rank, with each rank having an x4 data width. Dual-rank modules can offer performance advantages in certain scenarios by allowing the memory controller to interleave accesses between ranks, effectively increasing the data transfer rate. The CAS Latency (CL) of 22 indicates the number of clock cycles it takes for the memory to respond to a column address command, which is a standard specification for DDR4-3200 modules. This memory module is specifically designed and tested for use within Lenovo's ecosystem, ensuring compatibility and optimal performance. Upgrading to this 64GB module can significantly improve system responsiveness, accelerate data processing, and enhance the overall user experience for memory-intensive tasks such as virtualization, large dataset analysis, content creation, and complex simulations.

Condition:Refurbished

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