Specifications
Part Number
SDS6BA176OSP9X7
Series
SanDisk DC SN861
Capacity
7.68 TB
Form Factor
U.2 (2.5-inch, 15 mm); E1.S variants available
Interface
PCIe Gen5 x4, NVMe v2.0
Endurance
1 DWPD (read-intensive)
Sequential Read
13,700 MB/s max (128 KiB)
Sequential Write
7,500 MB/s max (128 KiB)
Random Read
Up to 3,300K IOPS (4 KiB)
Security
SE, ISE, TCG Opal 2.01
Operating Power
< 20 W average
Idle Power
< 5 W average
Dimensions
69.85 mm × 100.45 mm
Operating Temp
0 °C to 70 °C ambient
Non-operating Temp
-40 °C to 85 °C
AFR (projected)
0.35%
Warranty
5 years
Recognition
OCP Inspired; listed on the OCP Marketplace
Sibling SKU (Lenovo)
4XB7B10398 (ThinkSystem 2.5-inch U.2 SN861 7.68TB)
Overview
The SanDisk DC SN861 SDS6BA176OSP9X7 is a 7.68 TB read-intensive data center NVMe SSD built on a PCIe Gen5 x4 interface. SanDisk positions the SN861 specifically against the AI Data Cycle - model training, interface prompting and inference engines - where the storage tier has to keep a GPU-heavy node from stalling on I/O.
Sequential read tops out at 13,700 MB/s with up to 3,300K random-read IOPS, and the drive holds under 20 W average operating power with sub-5 W idle. That IOPS-per-watt ratio is the design point: in a 24-drive node, the storage tier's power budget is what decides how much of the platform envelope is left for accelerators.
The SN861 family spans 1.60 TB / 3.20 TB / 6.40 TB at 3 DWPD and 1.92 TB / 3.84 TB / 7.68 TB at 1 DWPD in U.2, plus E1.S 9.5 mm and 15 mm variants up to 15.36 TB. TCG Opal 2.01, SE and ISE security modes are available across capacities. The platform holds OCP Inspired recognition and is listed on the OCP Marketplace.
Key Benefits
Gen5 bandwidth at low power — 13,700 MB/s read under a 20 W average envelope. AI-cycle design point — built for training staging and inference stores. 3,300K random-read IOPS — high-concurrency read tiers. OCP Inspired — reviewed against the Datacenter NVMe SSD specification.
Applications
AI/ML training and inference data tiers, hyperscale and cloud block storage, big-data analytics and modelling, HPC scratch volumes, and high-CPU/high-GPU node local storage.
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