Safe Checkout
Secure Payments
Fast Delivery
Order Today
Free Shipping
Across the US
Easy Returns
Hassle-Free
HP QG272AV 64GB Kit (4x16GB) DDR3-1600MHz RDIMM 2Rx4 CL11 Memory
- Total Capacity: 64GB Kit
- Module Capacity: 4 x 16GB
- Type: DDR3 SDRAM
- Form Factor: RDIMM (Registered DIMM)
- Speed: 1600MHz (PC3-12800)
- Latency: CL11
- Rank: 2Rx4 (Dual Rank x4)
- Manufacturer: HP
Click on Inquire to get latest price
Free U.S. Ground Shipping
Typically 1-2 handling + 3-7 transit days
Purchase orders accepted
For government, enterprise, data center, and small business customers.
Bulk Purchase Inquiry
Volume pricing and availability
Product Overview
The HP QG272AV is a 64GB memory kit consisting of four 16GB DDR3 RDIMMs. Each module operates at 1600MHz with a CL11 latency, designed for high-performance servers and workstations.
Technical Information
| Total Capacity | 64GB Kit |
| Module Capacity | 4 x 16GB |
| Memory Type | DDR3 SDRAM |
| Form Factor | RDIMM |
| Speed | 1600MHz |
Additional Specifications
| CAS Latency | CL11 |
| Rank | 2Rx4 |
| Manufacturer | HP |
| Part Number | QG272AV |
Product Description
This HP QG272AV memory kit provides a substantial 64GB of RAM, configured as four individual 16GB DDR3 Registered DIMMs (RDIMMs). RDIMMs are designed to reduce the electrical load on the memory controller, enabling systems to support higher memory capacities and maintain stability under heavy workloads, making them ideal for servers and high-end workstations. The memory operates at a speed of 1600MHz (PC3-12800), offering excellent bandwidth for demanding applications and multitasking environments. The CAS Latency is CL11, which is a standard timing for this speed class, balancing responsiveness with performance. The 2Rx4 configuration indicates that each module is dual-ranked with four data bits per memory chip, optimizing memory access. This kit is specifically designed to enhance the performance and capacity of compatible HP servers and workstations. By providing a large pool of fast, reliable memory, it can significantly improve application performance, enable larger datasets to be processed in memory, and contribute to overall system efficiency.
