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NVIDIA Virtual Compute Server

Powering the Most Compute-Intensive Server Workloads


AI, deep learning, and data science workflows require an unprecedented amount of compute power. NVIDIA Virtual Compute Server (vCS) enables data centers to accelerate server virtualization with the latest NVIDIA data center GPUs, including NVIDIA A100 and A30 Tensor Core GPUs, 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. This isn’t a marginal step for virtualization — it’s a big leap.

vCS Solution Overview


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. 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.


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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.


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Learn more about how NVIDIA Virtual Compute Server helps maximize performance and simplify IT management.

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.


How is vCS different from NVIDIA vPC/vApps and NVIDIA RTX vWS?

NVIDIA Virtual PC (vPC) and Virtual Apps (vApps) and NVIDIA RTX Virtual Workstation (vWS) are client compute products for virtual graphics that are designed for knowledge workers and creative or technical professionals. vCS is for compute-intensive server workloads, such as AI, deep learning, and data science.

Is vCS licensed the same way as NVIDIA vPC/vApps and NVIDIA RTX vWS?

No, vCS is licensed differently than vPC/vApps and vWS. vPC/vApps and vWS are licensed by concurrent users (CCU), either as a perpetual license or yearly subscription. Since vCS is for server compute workloads, the license is tied to the GPU rather than a user. As such, vCS is licensed per GPU as a yearly subscription. Additional details on licensing can be found in the NVIDIA Virtual GPU Packaging, Pricing and Licensing Guide.

Which NVIDIA GPUs are supported with vCS?

Please refer to the Supported Products link under the software release version on the NVIDIA vGPU Software Documentation page for a detailed support matrix. vCS is supported with the NVIDIA A100, A40, A30 and A10 GPUs.

Which hypervisors are supported with vCS?

Please refer to the Supported Products link under the software release version on the NVIDIA vGPU Software Documentation page for a detailed support matrix. Red Hat RHV/RHEL, and Nutanix AHV support NVIDIA vCS. VMware vSphere is supported with the NVIDIA AI Enterprise software suite.

Which servers are certified to run vCS?

Refer to the vGPU-Certified Servers page for a full list of certified servers for all vGPU products.

Can containers be used with vCS?

Yes, containers can be run in VMs with vCS. NVIDIA NGC offers a comprehensive catalog of GPU-accelerated containers for deep learning, machine learning, and HPC. Workloads can also be run directly in a VM, without containers, using vCS.


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

See the full list of recommended NVIDIA GPUs for virtualization.

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