NVIDIA Virtual Compute Server

Powering the Most Compute-Intensive Server Workloads for KVM-based infrastructures

Maximum Performance to Maximize Workloads

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.

NVIDIA AI Enterprise

NVIDIA AI Enterprise is an end-to-end, cloud-native suite of AI and data analytics software, optimized to enable any organization to use AI, certified to deploy anywhere – from the enterprise data center to the public cloud, and includes global enterprise support to keep AI projects on track. For virtualized data centers running VMware, NVIDIA AI Enterprise is optimized, certified, and supported on VMware vSphere with Tanzu and VMware Cloud Foundation with Tanzu.

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Upscaled for Maximum Efficiency

NVIDIA Virtual GPUs give you near bare metal performance in a virtualized environment, maximum utilization, management and monitoring, in a KVM-based hypervisor 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. Our 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.

Resources For IT Managers

Learn more about how NVIDIA Virtual Compute Server helps maximize performance and simplify IT management in KVM-based infrastructures.

Utilization Optimization

Utilization Optimization

Take advantage of valuable GPU resources to seamlessly provision GPU sharing for lighter workloads like inference, or multiple virtual GPUs for more compute-intensive workloads like deep learning training.

Manageability and Monitoring

Manageability and Monitoring

Ensure high availability and uptime of the systems relied on by data scientists and researchers. Easily monitor GPU  performance at the guest, host, and application level. You can even leverage management tools like suspend/resume and live migration. Learn more about the operational benefits of GPU virtualization.

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Browse Supported GPUs

Virtual Compute Server is supported with the most powerful NVIDIA GPUs available, including the NVIDIA A100 Tensor Core GPU, NVIDIA A30 Tensor Core GPU, NVIDIA A40 GPU, NVIDIA T4 GPU, and the NVIDIA V100 Tensor Core GPU.

See the full list of recommended NVIDIA GPUs for virtualization.

NVIDIA partners with a broad ecosystem of OEM partners who can be found on the Find a Certified Server page.

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Buy vCS

Purchase the NVIDIA vGPU solution from an NVIDIA Partner Network (NPN) partner. Our extensive network of trained partners can help with architecting and deploying an optimal accelerated virtualized environment.

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