Samsung 1.92TB M.2 2000 MB/s PCIe 3.0 x4 SSD

The Samsung MZ1LW1T9HMLS is a 1.92TB M.2 NVMe solid state drive that connects through a PCIe 3.0 x4 interface, bringing data straight to the CPU at up to 2000 MB per second for reads and 1200 MB per second for writes. It drops easily into any system with an open M.2 slot that supports PCIe, making it a straightforward upgrade for servers, storage nodes, or high-end workstations that need faster access to critical data.

Inside busy virtual machines, transactional databases, or analytics platforms, this drive keeps queues moving with up to 430,000 random read IOPS and 40,000 random write IOPS. Applications that rely on rapid, repeated access to small files—such as log aggregation, web hosting, or real-time monitoring—benefit most from this level of responsiveness because latency stays low even when demand spikes.

An endurance rating of 1.3 drive writes per day strikes a balance between lifespan and capacity. System administrators can schedule backups, indexing, and other write-heavy tasks without worrying about prematurely wearing out the media. The drive is designed to run around the clock in dense data center racks, edge computing enclosures, and on-premise clusters where downtime is not an option.

Because it rides on PCIe 3.0, the MZ1LW1T9HMLS delivers its full performance without requiring the latest motherboard revision, so legacy servers can gain a meaningful speed boost without a full platform refresh. For new builds, it slides in as a reliable primary boot volume or a caching layer in front of slower cold storage, reducing load times and accelerating overall system throughput.

• 1.92TB capacity in a space-saving M.2 form factor
• PCIe 3.0 x4 NVMe interface for direct, low-latency communication with the host
• Up to 2000 MB/s sequential read and 1200 MB/s sequential write speeds
• Random performance rated at 430,000 IOPS for reads and 40,000 IOPS for writes
• Endurance of 1.3 drive writes per day for dependable 24/7 operation
• Ideal for enterprise servers, virtualization clusters, and data-intensive applications