NVIDIA vCS ____________________________________________________________________________________
Virtualize Compute for AI, Deep Learning, and Data Science
NVIDIA Virtual Compute Server (vCS) software enables data centers to accelerate server virtualization with the latest NVIDIA data center GPUs, including A100 Tensor Core GPU, so that the most compute-intensive workloads, such as artificial intelligence, deep learning, and data science, can be run in a virtual machine.
1 Support for NVIDIA A100 Tensor Core GPU will be available in an upcoming vGPU 2020 release.
NVIDIA vCS Features:
- GPU Sharing: GPU sharing (fractional) is only possible with NVIDIA vGPU technology. It enables multiple VMs to share a GPU, maximizing utilization for lighter workloads that require GPU acceleration.
- GPU Aggregation: With GPU aggregation, a VM can access more than one GPU, which is often required for compute-intensive workloads. vCS supports both multi-vGPU and peer-to-peer computing. With multi-vGPU, the GPUs aren’t directly connected; with peer-to-peer, they are through NVLink for higher bandwidth.
- Management and Monitoring: vCS provides support for app-, guest-, and host-level monitoring. In addition, proactive management features provide the ability to do live migration, suspend and resume, and create thresholds that expose consumption trends impacting user experiences, all through the vGPU management SDK.
- NGC: NVIDIA GPU Cloud (NGC) is a hub for GPU-optimized software that simplifies workflows for deep learning, machine learning, and HPC, and now supports virtualized environments with NVIDIA vCS.
- Peer-to-Peer Computing: NVIDIA® NVLink™ is a high-speed, direct GPU-to-GPU interconnect that provides higher bandwidth, more links, and improved scalability for multi-GPU system configurations—now supported virtually with NVIDIA virtual GPU (vGPU) technology.
- ECC & Page Retirement: Error correction code (ECC) and page retirement provide higher reliability for compute applications that are sensitive to data corruption. They’re especially important in large-scale cluster-computing environments where GPUs process very large datasets and/or run applications for extended periods.
- Multi-Instance GPU (MIG): Multi-Instance GPU (MIG) is a revolutionary technology that can extend the capabilities of the data center that enables each NVIDIA A100 Tensor Core GPU¹ to be partitioned into up to seven instances, fully isolated and secured at the hardware level with their own high-bandwidth memory, cache, and compute cores. With vCS software, a VM can be run on each of these MIG instances so that organizations can take advantage of management, monitoring, and operational benefits of hypervisor-based server virtualization.