PNY NVIDIA Tesla M10 - GPU Card - 4 GPUs TCSM10M-PB - TCSM10M-PB

PNY NVIDIA Tesla M10. Graphics processor family: NVIDIA, Graphics processor: Tesla M10. Discrete graphics adapter memory: 32 GB, Graphics adapter memory type: GDDR5, Memory bus: 256 bit. Interface type: PCI Express x16 3.0. Cooling type: Passive

https://www.pny.eu/consumer/explore-all-products/nvidia-tesla/755-nvidia-tesla-m10

GREAT USER EXPERIENCE. HIGH USER DENSITY

IT needs a software-defined approach to virtualization in the data center that delivers scalability, data security, and simplified manageability. However, adoption of desktop virtualization solutions has been limited by their failure to deliver a user experience thatΓÇÖsas good as physical devices.
The NVIDIA Tesla M10 GPU accelerator works with NVIDIA GRIDΓäó software to provide the industryΓÇÖs highest user density for virtualized desktops and applications. It supports 64 desktops per board and 128 desktops per server, giving your business the power to deliver great experiences to all of your employees at an affordable cost.

GRID technology helps you protect your most valuable data, keeping it secure in the data center while providing just the right level of access to users. Keep your corporate intellectual property safe while also boosting productivity across the enterprise. NVIDIA GRID has three different editions that let you assign the right level of resources to each of your users; Virtual PC, Virtual Workstation, and Virtual Workstation Extended. When you create a new desktop, you simply assign a license based on the user profile that will be accessing it. IT managers can easily assign the optimal amount of graphics memory and deliver a customized graphics profile to meet the specific needs of each user. Every virtual desktop has dedicated graphics memory, just like they would at their desk, so they always have the resources they need to launch and run their applications. GRID enables up to sixteen users to share each physical GPU, so the graphics resources of the available GPUs can be assigned to virtual machines in a balanced way.