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Intel Vision Accelerator Design with Intel Movidius VPU
As QNAP NAS evolves to support a wider range of applications (including surveillance, virtualization, and AI) you not only need more storage space on your NAS, but also require the NAS to have greater power to optimize targeted workloads. The Mustang-V100 is a PCIe-based accelerator card using an Intel Movidius VPU that drives the demanding workloads of modern computer vision and AI applications. It can be installed in a PC or compatible QNAP NAS to boost performance as a perfect choice for AI deep learning inference workloads.
Available Models : Mustang-V100-MX8-R10
Computing Accelerator Card with 8 x Movidius Myriad X MA2485 VPU, PCIe Gen2 x4 interface, RoHS
OpenVINO toolkit
OpenVINO toolkit is based on convolutional neural networks (CNN), the toolkit extends workloads across Intel hardware and maximizes performance. It can optimize pre-trained deep learning model such as Caffe, MXNET, Tensorflow into IR binary file then execute the inference engine across Intel-hardware heterogeneously such as CPU, GPU, Intel Movidius Neural Compute Stick, and FPGA.
Get deep learning acceleration on Intel-based Server/PC
You can insert the Mustang-V100 into a PC/workstation running Linux (Ubuntu) to acquire computational acceleration for optimal application performance such as deep learning inference, video streaming, and data center. As an ideal acceleration solution for real-time AI inference, the Mustang-V100 can also work with Intel OpenVINO toolkit to optimize inference workloads for image classification and computer vision.
QNAP NAS as an Inference Server
OpenVINO toolkit extends workloads across Intel hardware (including accelerators) and maximizes performance. When used with QNAPs OpenVINO Workflow Consolidation Tool, the Intel-based QNAP NAS presents an ideal Inference Server that assists organizations in quickly building an inference system. Providing a model optimizer and inference engine, the OpenVINO toolkit is easy to use and flexible for high-performance, low-latency computer vision that improves deep learning inference. AI developers can deploy trained models on a QNAP NAS for inference, and install the Mustang-V100 to achieve optimal performance for running inference.
Note: QTS 4.4.0 (or later) and OWCT v1.1.0 are required for the QNAP NAS.
Easy-to-manage Inference Engine with QNAP OWCT
Upload a video file
Download inference result
Check Compatible NAS Models
| 28-Bay | TS-2888X |
| 24-Bay | TVS-2472XU-RP |
| 16-Bay | TVS-1672XU-RP |
| 12-Bay | TVS-1272XU-RP |
| 9-Bay | TVS-972XU, TVS-972XU-RP |
| 8-Bay | TVS-872XT, TVS-872XU, TVS-872XU-RP |
| 6-Bay | TVS-672XT |
Dimensions (Unit: mm)
| Main Chip | Eight Intel Movidius Myriad X MA2485 VPU |
| Operating Systems | PC: Ubuntu 16.04.3 LTS 64-bit, CentOS 7.4 64-bit, Windows 10 (More OS are coming soon) NAS: QTS (Installing Mustang Card User Driver in the App Center is required.) |
| Dataplane Interface | PCI Express x4 Compliant with PCI Express Specification V2.0 |
| Power Consumption (W) | <30W |
| Operating Temperature & Relative Humidity | 5C~55C (ambient temperature)5% ~ 90% |
| Cooling | Active fan: 47 x 47 x 9.4 mm |
| Dimensions | 169.54 mm x 80.05 mm x 23.16 mm |
| Power Connector | *Preserved PCIe 6-pin 12V external power |
| Dip Switch/LED indicator | Up to 16 cards can be supported with operating systems other than QTS; QNAP TS-2888X NAS supports up to 8 cards. Please assign a card ID number (from 0 to 15) to the Mustang-V100 by using rotary switch manually. The card ID number assigned here will be shown on the LED display of the card after power-up. |
