October 9th, 2018 by Marshall Gunnell
NVIDIA Announces Quadro Virtual Data Center Workstation Enhancements
NVIDIA announced today the latest enhancements to the Quadro Virtual Data Center Workstations (Quadro vDWS), aimed to accelerate demanding graphics and compute workflows by delivering high performance of a virtual workstation.
Using two Tesla V100 GPUs, creative professional working on remote, virtual workstations are able to render compelling, realistic visualizations – up to 94% faster. Compared to a CPU-only system, utilizing two Tesla V100s enables engineers and designers to reduce their time to market and can complete simulations up to 7 times faster. Additionally, professionals are able to work at remote locations while their IP and their work are secured in the data center.
New capabilities include:
- Run Multi-GPU Workloads with NVIDIA Quadro vDWS– Aggregate up to four NVIDIA Tesla GPUs in a single Virtual Machine (VM) for the most graphics and compute-intensive rendering, simulation, and design workflows. Aggregation of multiple Tesla GPUs, in addition to GPU sharing across multiple virtual machines, is now supported.
- Live Migration with VMware vMotion– IT can migrate live, NVIDIA GPU-accelerated VM’s without impacting users or requiring downtime, saving both time and resources. This feature is now supported on the Quadro vDWS and GRID vPC and GRID vApps software products with VMware vMotion, vSphere 6.7 u1.
- Support for NVIDIA Tesla T4 GPUs– Framebuffer is doubled while in the same low-profile, single-slot form factor as the previous generation Tesla P4. The new 70W Tesla T4 enables demanding workflows in a VDI environment including advanced rendering, simulation, and design when combined with multi-GPU support.
- AI workloads on virtual machines with NVIDIA GPU Cloud (NGC)– NGC empowers AI researchers with GPU-accelerated deep learning containers for TensorFlow, PyTorch, MXNet, TensorRT, and more. Now tested with the latest release of Quadro vDWS, these pre-integrated, GPU-accelerated containers include NVIDIA CUDA Toolkit, NVIDIA deep learning libraries, and an operating system.
Support for Multi-GPU, VMware VMotion, and NGC containers is expected late Fall 2018. NVIDIA Tesla T4 support with NVIDIA vGPU software is expected by year end of 2018.