Other Components Asus

GX601 ROG Strix Helios HDD Cage KitTwo bay 3.5 HDD cage kit for the Strix Helios series. The trays are tool-less drive tray design for 3.5" drives, simply attach them to the tray and slide them into the cage without using tools or screws. The 3.5-inch drive trays can accommodate 2.5-inch drives but you would need to use screws to secure them in place. The design ideal is for use with ROG Strix Helios cases which end users request to support more storage demand. Type3.5"/2.5" HDD/SSD FrameFits inAsus Strix Helios CasesDesigned for3.5" or 2.5" drivesScrews are needed to secure 2.5" drives in placeMaterialSteel, ABS Plastic, RubberWeight/Dimensions370g(LxWxH) 168 x 117 x 83 mmPackage Contents1 …

ASUS Hyper M.2 x16 Gen 4.Host interface:PCIe, Output interface:M.2, Expansion card standard:PCIe 4.0.Product colour:Silver, Purpose:PC, Cooling type:Active.Data transfer rate (max):256 Gbit/s.Width:270 mm, Depth:122 mm, Height:15 mm.Packaging type:Box.Specifications:-Host interface:PCIe.-Number of M.2 slots (M):4.-Expansion card standard:PCIe 4.0.-Output interface:M.2.-Depth:122 mm.-Width:270 mm.-Height:15 mm.-Weight:322 g.-Packing type:Box.-Quantity 1.-Data transfer speed:256 Gbps.-Number of expansion slots:4.-Simplified installation:Yes.-Use with:PC.-Product color:Silver.-Internal:Yes.-Cooling type:Active.-LED indicators:Access.-Number of fans:1 Fan(s).-Data transfer rate (max):256 Gbit/s.

ASUS Hyper M.2 x16 Gen5 Card (PCIe 5.0/4.0) supports up to four NVMe M.2 (2242/2260/2280/22110) devices at up to 512 Gbps for RAID functions across diverse CPU platforms
- 1 x 6-pin PCIe power connector and two-phase power solution with up to 14-watt output to support the latest NVMe drives
- Large heatsink and active fan reduces M.2 SSD temperatures for unthrottled transfer speeds and enhanced reliability; additional fan cable connects to the motherboard's chassis fan header to allow the motherboard to control and regulate fan speed
- Adapted server-grade PCB supports up to four PCIe 5.0/4.0 M.2 drives with up to 512 Gbps bandwidth for smooth data transfers   …

ASUS HYPER M.2 X16 CARD V2.Host interface:PCIe, Output interface:M.2, Expansion card standard:PCIe 3.0.Bandwidth:128 Gbit/s.Width:202 mm, Depth:13 mm, Height:96 mm.Specifications:-Host interface:PCIe.-Number of M.2 slots (M):1.-Expansion card standard:PCIe 3.0.-Output interface:M.2.-Depth:13 mm.-Width:202 mm.-Height:96 mm.-User manual:Yes.-Quantity 1.-Internal:Yes.-Bandwidth:128 Gbit/s.

The ROG Herculx packs the structural integrity to bear the burden of even the heaviest graphics card. Toolless installation, precision adjustment, and customizable RGB lighting are combined into a space-saving design that neatly slides in to protect your investment in GPU horsepower.Asus ROG Herculx Graphics Card Holder at a GlanceStand design is compatible with a variety of chassis and doesn’t occupy PCIe slots. Offers a support height of 72-128 mm, delivering a strong, adaptable design.  Supports NVIDIA GeForce RTX 40 series graphics cards. An adjustable wheel and release button enable easy, toolless setup and removal.  Included spirit level simplifies setup. Embedded 3D ARGB element is comp…

Enable blisteringly fast speeds and any-way-up connectivity ASUS ThunderboltEX 4 is a Thunderbolt™ 4 add-on card that offers 40 Gbps bi-directional bandwidth, USB4™ device compatibility, and DisplayPort™ 1.4. It supports up to five Thunderbolt devices in a daisy chain and enables up to 100-watt USB-C® charging for laptops.

AI accelerator (NPU):- ASUS Tinker Edge R is an Single Board Computer (SBC) specially designed for AI applications. It uses Rockchip NPU, a Machine Learning (ML) accelerator that speeds up processing efficiency, lowers power demands and makes it easier to build connected devices and intelligent applications. With this integrated Machine Learning (ML) accelerator, the Tinker Edge R is capable of performing 3 tera-operations per second (TOPS), using low power consumption. And it's optimized for Neural Network (NN) architecture, which means Tinker Edge R can support multiple Machine Learning (ML) frameworks and let lots common Machine Learning (ML) models can be easily compiled and run on the Tinker …