Jan. 09, 2023 - Micron Technology today announced the Micron 9400 NVMe™ SSD designed to manage the most demanding data center workloads, particularly in artificial intelligence (AI) training, machine learning (ML) and high-performance computing (HPC) applications. The Micron 9400 PCIe Gen4 NVMe SSD is available in a U.3 form factor that is backwards-compatible with U.2 sockets and comes in capacities ranging from 6.4TB to 30.72TB.
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Micron 9400 PRO Read-Intensive, 1 DWPD
Micron 9400 MAX Mixed-Use, 3 DWPD
Capacity
7.68TB 15.36TB 30.72TB
6.4TB 12.8TB 25.6TB
Form Factor
U.3 2.5" 15mm
Interface
PCIe Gen4 NVMe 1.4
NAND
Micron 176-layer 3D TLC NAND
Seq. Read (MB/s)1
7000
Seq. Write (MB/s)1
Random Read (IOPS)1
1.6M IOPS (7.68TB and 15.36TB) 1.5M IOPS (30.72TB)
1.6M IOPS (6.4TB and 12.8TB) 1.5M IOPS (25.6TB)
Random Write (IOPS)1
300K IOPS
600K IOPS (6.4TB and 12.8TB) 550K IOPS (25.6TB)
70/30 Random Read/Write (IOPS)1
770K IOPS (7.68TB) 780K IOPS (15.36TB) 770K IOPS (30.72TB)
930K IOPS (6.4TB) 940K IOPS (12.8TB) 900K IOPS (25.6TB)
Latency (typical, µs)2
69 (read) 10 (write)
1 Performance measured under the following conditions: Steady-state as defined by SNIA Solid State Storage Performance Test Specification Enterprise v1.1; Drive write cache enabled; NVMe power state 0; Sequential workloads measured using FIO with a 128K IO size and a queue depth of 32; Random read workloads measured using FIO with a 4K IO size and queue depth of 256; Random write workloads measured using FIO with a 4K IO size and a queue depth of 128. 2 Latency values measured with random workloads using FIO, 4KB transfers, queue depth = 1; Typical latency = median, 50th percentile.
High performance, capacity and low latency are critical features for enterprises seeking to maximize their investments in AI/ML and supercomputing systems," said Alvaro Toledo, vice president and general manager of data center storage at Micron. "Thanks to its industry-leading 30TB capacity and stunning performance with over 1 million IOPS in mixed workloads, the Micron 9400 SSD packs larger datasets into each server and accelerates machine learning training, which equips users to squeeze more out of their GPUs."
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