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Samsung M321R2GA3PB0-CWMKH 16GB DDR5-5600MHz RDIMM 1Rx8 CL46 Memory
- Capacity: 16GB.
- Type: DDR5.
- Speed: 5600MHz.
- Form Factor: RDIMM (Registered DIMM).
- Rank: 1Rx8 (Single Rank x8).
- Latency: CL46.
- Designed for high-performance servers and workstations.
- Utilizes advanced DDR5 technology for increased bandwidth and efficiency.
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Product Overview
The Samsung M321R2GA3PB0-CWMKH is a 16GB DDR5 RDIMM memory module. It operates at 5600MHz with a 1Rx8 configuration and CL46 latency, designed for next-generation servers.
Technical Information
| Product Type | Memory Module |
| Part Number | M321R2GA3PB0-CWMKH |
| Manufacturer | Samsung |
| Capacity | 16GB |
| Memory Type | DDR5 SDRAM |
Additional Specifications
| Speed | 5600MHz |
| Form Factor | RDIMM |
| Rank | 1Rx8 |
| CAS Latency | CL46 |
Product Description
The Samsung M321R2GA3PB0-CWMKH is a 16GB DDR5 Registered DIMM (RDIMM) memory module, engineered to deliver cutting-edge performance for modern server environments. As a DDR5 module, it leverages the latest advancements in memory technology, offering higher data rates, improved power management, and enhanced signal integrity compared to previous generations. This makes it suitable for the most demanding computational tasks. Operating at a high frequency of 5600MHz, this memory module provides exceptional bandwidth, which is critical for applications that require rapid data processing and access. The 1Rx8 configuration indicates a single-rank design utilizing x8 DRAM devices, optimized for efficient data handling. The RDIMM form factor includes an onboard register that buffers address and command signals, reducing the load on the memory controller and enabling greater memory scalability and stability. With a CAS Latency (CL) of 46, the M321R2GA3PB0-CWMKH strikes a balance between its high operational speed and the timing required for data retrieval. This module is designed for compatibility with server platforms that support DDR5 RDIMMs, making it an excellent choice for workloads such as artificial intelligence, machine learning, big data analytics, and large-scale virtualization, where maximizing memory performance is paramount.
