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Product Overview |
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The Cisco UCS-ML-1X644RV-A is a 64GB DDR4 memory module designed for use in servers requiring stable and high-capacity RAM. It fits into systems that prioritize error correction and consistent performance, playing a key role in maintaining data accuracy and system reliability. |
General Information |
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| Brand | Cisco |
| Part Number | UCS-ML-1X644RV-A |
Technical Information |
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| Memory Capacity | 64GB |
| Memory Technology | DDR4 |
| RAM Standard | PC4-19200 |
| Pin Count | 288-Pin |
| Error Correction | ECC |
| Rank | 4Rx4 |
| Voltage | 1.2V |
Performance |
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| Bus Speed | 2400MHz |
| Bandwidth | 19200 MB/s |
| Native Speed | 2400MT/s |
Physical Characteristics |
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| Weight | 0.50 |
| Condition | Refurbished |
Miscellaneous |
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| Eco Friendly | Yes |
| Compliance Standards | RoHS, CE, FCC |
Product Description |
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This 64GB DDR4 RAM module is built for servers and workstations that require dependable and fast memory. It operates at 2400MHz, providing solid speed for handling demanding applications and multitasking. Often found in enterprise environments, it supports error correction (ECC) to help prevent data corruption, making it a reliable choice for data centers and critical computing tasks. Key Features
This module is typically deployed in servers that run complex databases, virtualization, and analytics. Its error correction and size help maintain smooth operation in environments where memory reliability is essential. |
Use Cases |
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The Cisco UCS-ML-1X644RV-A memory module is tailored for enterprise-grade server environments where data integrity and uptime are critical. It is especially suitable for organizations running virtualization workloads, large database operations, and high-performance computing tasks that require robust error correction and high memory capacity. How It's Used:
This memory module supports sustained operational efficiency by maintaining data accuracy and reducing system errors in demanding IT infrastructures. Its compatibility with Cisco UCS platforms ensures seamless integration and scalability for growing enterprise workloads. |