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Dell YNNJ1 1.6TB PCI Express NVMe 3.0 x4 TLC U.2 2.5-inch SSD
- Capacity: 1.6TB
- Interface: PCI Express NVMe 3.0 x4
- Form Factor: 2.5-inch U.2
- NAND Type: Triple-Level Cell (TLC)
- Designed for enterprise servers and storage
- High read/write speeds for demanding applications
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Product Overview
The Dell YNNJ1 is a high-performance 1.6TB NVMe SSD designed for demanding enterprise workloads. It utilizes the PCIe 3.0 x4 interface for rapid data transfer speeds and a U.2 form factor for easy integration into compatible server and storage systems. This drive features TLC NAND flash for a balance of performance and endurance.
Technical Information
| Capacity | 1.6TB |
| Interface | PCIe 3.0 x4 NVMe |
| Form Factor | 2.5-inch U.2 |
Additional Specifications
| NAND Type | TLC |
| Brand | Dell |
| SKU | YNNJ1 |
Product Description
The Dell YNNJ1 SSD represents a significant leap in storage performance, leveraging the NVMe protocol over a PCIe 3.0 x4 interface. This combination allows for substantially higher throughput and lower latency compared to traditional SATA SSDs, making it ideal for applications that require rapid access to large datasets. The 2.5-inch U.2 form factor ensures compatibility with a wide range of modern server and workstation chassis that support this interface. Built with Triple-Level Cell (TLC) NAND flash, the YNNJ1 strikes a balance between high density, cost-effectiveness, and robust performance for enterprise environments. While TLC offers excellent read and write speeds, its endurance characteristics are well-suited for typical server workloads where data is frequently accessed and written. The drive is engineered to meet the rigorous demands of data centers, high-performance computing, and mission-critical applications. This SSD is a crucial component for accelerating I/O-intensive tasks such as database operations, virtualization, big data analytics, and content creation. By providing a fast and reliable storage solution, the Dell YNNJ1 helps to reduce bottlenecks, improve application responsiveness, and enhance overall system efficiency, ultimately contributing to increased productivity and faster insights from data.



