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HP M0R81AR 32GB DDR4-2133MHz LRDIMM 4Rx4 CL15 Memory
- HP Memory Module
- Capacity: 32GB
- Type: DDR4
- Speed: 2133MHz
- Form Factor: LRDIMM (Load-Reduced DIMM)
- Rank: 4Rx4
- Latency: CL15
- Designed for servers and workstations
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Product Overview
This is an HP M0R81AR memory module, specifically a 32GB DDR4-2133MHz LRDIMM. It features 4 ranks (4Rx4) and CL15 latency, designed for high-performance server applications.
Technical Information
| Brand | HP |
| Part Number | M0R81AR |
| Memory Type | DDR4 SDRAM |
| Capacity | 32GB |
Additional Specifications
| Speed | 2133MHz |
| Form Factor | LRDIMM |
| Rank | 4Rx4 |
| CAS Latency | CL15 |
Product Description
The HP M0R81AR is a high-capacity 32GB memory module designed to enhance the performance of compatible HP servers and workstations. It utilizes DDR4 SDRAM technology, which offers significant improvements in speed, power efficiency, and density compared to previous DDR generations. The module operates at a frequency of 2133MHz, providing ample bandwidth for demanding applications and multitasking environments. This memory module is a Load-Reduced DIMM (LRDIMM), a type of RAM that incorporates a memory buffer chip. This buffer reduces the electrical load on the memory controller, allowing for higher memory capacities and more DIMMs to be populated per channel. The 4Rx4 configuration indicates that the module has four ranks, each with a 1-bit-wide data interface, which further optimizes performance and density. The CAS Latency of CL15 specifies the timing delay for column access, contributing to the overall responsiveness of the memory subsystem. As an enterprise-grade component, the M0R81AR is built for reliability and stability in mission-critical server environments. Its specifications make it suitable for memory-intensive tasks such as virtualization, large database operations, scientific computing, and high-performance analytics. By upgrading to this 32GB DDR4 LRDIMM, users can expect improved system responsiveness, faster data processing, and the ability to handle larger datasets and more complex workloads.


