NVIDIA Virtual Compute Server

Range/Series Overview

IMPORTANT: This Range/Series overview section describes the range of which this product belongs. Features of different versions might be mentioned and may not apply to the specific product on this page. Please view Specs for specification of product.

AI, deep learning, and data science workflows require an unprecedented amount of compute power. NVIDIA Virtual Compute Server (vCS) enables data centers running on Red Hat Enterprise Linux, Red Hat Virtualization, and other supported KVM-based hypervisors to accelerate server virtualization with the latest NVIDIA data center GPUs, including NVIDIA A100 Tensor Core GPU and A30, so that the most compute-intensive workloads, such as artificial intelligence, deep learning, and data science, can be run in a virtual machine (VM) powered by NVIDIA vGPU technology.

Features Summary

• NVIDIA AI Enterprise
  With NVIDIA AI Enterprise, enterprises access an end-to-end, cloud-native suite of AI and data analytics software that has been optimized, certified, and supported by NVIDIA to run on VMware vSphere with NVIDIA-Certified Systems.
• Upscaled for maximum efficiency
  NVIDIA Virtual GPUs give you near bare metal performance in a virtualized environment, maximum utilization, management and monitoring, in a hypervisor-based virtualization environment for GPU-accelerated AI.
• Deep learning training performance scaling with vCS on NVIDIA A100 Tensor Core GPUs
  Developers, data scientists, researchers, and students need a massive amount of compute power for deep learning training. The A100 Tensor Core GPU accelerates the workload, letting them do more faster. NVIDIA software, the Virtual Compute Server, delivers nearly the same performance as bare metal, even when scaling to large deep learning training models that use multiple GPUs.
• Deep learning inference throughput performance with MIG on NVIDIA A100 Tensor Core GPUs using vCS
  Multi-instance GPU (MIG) is a technology, only found on the NVIDIA A100 Tensor Core GPU, that partitions the A100 GPU into as many as seven instances, each fully isolated with their own high-bandwidth memory, cache, and compute cores. MIG can be used with Virtual Compute Server, one VM per MIG instance. The performance is consistent when running an inference workload across multiple MIG instances on both bare metal and virtualized with vCS.

Specification Summary