Super Talent Technology 16GB Mini PCIe SSD Datasheet

Super Talent Technology
Brand
Super Talent Technology
Model
16GB Mini PCIe SSD
Category
Solid state drives
Type
Datasheet

This manual is also suitable for

Super Talent Technology 16GB Mini PCIe SSD is a high-performance storage solution designed for use in small form-factor devices such as netbooks and embedded systems. It features a SATA-II (3Gbps) interface and utilizes either MLC or SLC NAND flash memory to deliver fast data transfer speeds of up to 150 MB/s read and 100 MB/s write (MLC) or 170 MB/s read and 130 MB/s write (SLC). With its compact dimensions of 32.1 mm x 69.5 mm x 3.5 mm, it can easily fit into tight spaces.

Super Talent Technology 16GB Mini PCIe SSD is a high-performance storage solution designed for use in small form-factor devices such as netbooks and embedded systems. It features a SATA-II (3Gbps) interface and utilizes either MLC or SLC NAND flash memory to deliver fast data transfer speeds of up to 150 MB/s read and 100 MB/s write (MLC) or 170 MB/s read and 130 MB/s write (SLC). With its compact dimensions of 32.1 mm x 69.5 mm x 3.5 mm, it can easily fit into tight spaces.

www.supertalent.com
Over vie w
SATA Mini PCIe 2 Solid State Drive (SSD)
es
tur
© 2008 Super Talent Technology. Specifications subject to change without notice.
*Usable capacity may be less than specified after formatting.
p.1
ssd.supertalent.com
SATA-II Interface, Mini PCIe Form Factor
Physical Specifications
MLC
*Capacity
16GB – 64GB
Dimension
32.1 mm x 69.5 mm x 3.5 mm
Interface
SATA-II (3Gbps)
NAND Flash
MLC
Power Supply
3.3V
cc
±
5%
Operating Temp.
0°C - 70°C
SLC
*Capacity
16GB – 32GB
Dimension
32.1 mm x 69.5 mm x 3.5 mm
Interface
SATA-II (3Gbps)
NAND Flash
SLC
Power Supply
3.3V
cc
±
5%
Operating Temp.
0°C - 70°C
Performance Specifications
MLC
Sequential Read Rate
150 MB/sec (max)
Sequential Write Rate
100 MB/sec (max)
SLC
Sequential Read Rate
170 MB/sec (max)
Sequential Write Rate
130 MB/sec (max)
Ordering Information
MLC
16GB
FPM16GLSE
32GB
FPM32GLSE
SLC
8GB
FPD8GLSE
16GB
FPD16GLSE
The SATA Mini 2 PCIe SSD is an upgrade module specifically
designed to be a drop in replacement for the ASUS Eee PC S101,
900, 900A, and 901 netbook PCs. The modules are available
with both MLC and SLC Flash memory. The modules are simple
and easy to install for immediate capacity and performance benefits.
These modules are the fastest SATA Mini PCIe SSDs available
in the market. Delivering over 5x the performance of the original
SSDs. These products also make excellent high performance small
form factor products for embedded system designers.
The MLC based products are available in 16GB, 32GB, and 64GB
capacities and feature sequential read speeds up to 150 MB/s and
sequential write speeds up to 100 MB/s. The MLC drives come
with SuperTalent’s 2 year warranty.
For customers with greater endurance requirements or faster write
performance needs we also offer the SLC version. Available in
16GB and 32GB capacities and featuring sequential read speeds up
to 170 MB/s and sequential write speeds up to 130 MB/s. The SLC
products come with a 3 year warranty.
SATA Mini 2 PCIe SSD
ASUS Eee PC Compatible BIOS
Model BIOS Rev
900
0906
900A
0703
901
1808
901 GO
Any
S101
Any
64GB
FPM64GLSE
  • Page 1 1

Super Talent Technology 16GB Mini PCIe SSD Datasheet

Super Talent Technology
Brand
Super Talent Technology
Model
16GB Mini PCIe SSD
Category
Solid state drives
Type
Datasheet
This manual is also suitable for

Super Talent Technology 16GB Mini PCIe SSD is a high-performance storage solution designed for use in small form-factor devices such as netbooks and embedded systems. It features a SATA-II (3Gbps) interface and utilizes either MLC or SLC NAND flash memory to deliver fast data transfer speeds of up to 150 MB/s read and 100 MB/s write (MLC) or 170 MB/s read and 130 MB/s write (SLC). With its compact dimensions of 32.1 mm x 69.5 mm x 3.5 mm, it can easily fit into tight spaces.

Download PDF
report

Ask a question and I''ll find the answer in the document

Finding information in a document is now easier with AI

Related papers

Other documents