Scan AI

Virtual Compute Server

Virtual Compute Server - by 3XS Cloud and NVIDIA

Virtual Compute Server (vCS) enables the benefits of hypervisor-based server virtualisation for GPU-accelerated servers. Datacentre admins are now able to power any compute-intensive workload with GPUs in a virtual machine (VM). vCS software virtualises NVIDIA GPUs to accelerate large workloads, including more than 600 GPU-accelerated applications for AI, deep learning, and HPC.

As an NVIDIA Preferred Virtualisation partner, the 3XS Cloud team is able to advise on a wide range of virtual GPU topics including your choice of licences and the hardware required to deliver the platform on - specifically tailored for your number of users.

With GPU sharing, multiple VMs can be powered by a single GPU, maximising utilisation and affordability, or a single VM can be powered by multiple virtual GPUs, making even the most intensive workloads possible.

  • Maximise utilisation - increase utilisation and productivity with both GPU sharing and aggregation of multiple GPUs
  • Live migration - live migrate GPU-accelerated VMs without disruption, easing maintenance and upgrades
  • Security - extend the benefits of server virtualisation to GPU workloads
  • Multi-tenant - isolate workloads and securely support multiple users
  • Rapid deployment - leverage GPU-optimised NGC containers for AI, data science, and HPC
  • Reliability - prevent against data corruption with error-correcting code (ECC) and dynamic page retirement
  • Enterprise software support - get support with NVIDIA Enterprise and NVIDIA NGC Support Services

GPU Sharing

Fractional GPU sharing possible with NVIDIA vGPU technology. It enables multiple VMs to share a GPU, maximising utilisation for lighter workloads that require GPU acceleration. Up to 32 users can share a single GPU.


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. Using NVLink for higher bandwidth.


Management and Monitoring

vCS provides support for app-, guest-, and host-level monitoring. Proactive management features provide the ability to do live migration, suspend and resume, and create thresholds that expose consumption trends impacting user experiences.



NVIDIA GPU Cloud (NGC) is a hub for GPU-optimised software that simplifies workflows for deep learning, machine learning, and HPC, and now supports virtualised environments with NVIDIA vCS.


Peer-to-Peer Computing

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

Multi-Instance GPU (MIG) is a revolutionary technology that can extend the capabilities of the datacentre that enables each NVIDIA A100 GPU 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.



GPUDirect uses remote direct memory access (RDMA) technology to enable network devices to directly access GPU memory, bypassing CPU host memory, decreasing GPU-to-GPU communication latency, and completely offloading the CPU.

NVIDIA vCS Features

Configuration NVIDIA vCS
Windows OS Support  
Linux OS Support chart check
NVIDIA Compute Driver chart check
ECC reporting and handling chart check
Multi-GPU Support chart check
Maximum Hardware Rendering 1X 4K
Maximum resolution 4096 x 2160
Management and Support NVIDIA vCS
Host / Guest / Application-Level Monitoring chart check
Live Migration chart check
NVIDIA Direct Enterprise-Level Technical Support chart check
Maintenance Releases, Defect Resolutions and Security Patches chart check
NGC Support Services chart check
GPU A100 A30 A40 A10 A16
GPU Architecture Ampere Ampere Ampere Ampere Ampere
Memory Size 40 GB HBM2 24 GB GDDR6 48 GB GDDR6 24 GB GDDR6 4x 16 GB per GPU
Virtualisation Workload Highest performance virtualised compute including AI, HPC and data processing, includes support for up to 7 MIG instances. Virtualise mainstream compute and AI inference, includes support for up to 4 MIG instances. Mid-range to high-end 3D design and creative workflows with NVIDIA RTX Virtual Workstation (vWS). Entry to mid-range virtual workstations for design and engineering with NVIDIA vWS. Also run graphics-rich virtual desktops with vPC and deep learning inferencing with vCS. Office productivity applications, streaming video and teleconferencing tools for graphics-rich virtual desktops accessible from anywhere.
Usage vCS vCS vWS, vPC, vApps vWS, vPC, vApps, vCS vWS, vPC, vApps

For full GPU specifications and to compare models please see our Professional GPU Buyers Guide.

GPU V100 RTX 8000 RTX 6000 T4
GPU Architecture Volta Turing Turing Turing
Memory Size 32/16 GB HBM2 48 GB GDDR6 24 GB GDDR6 16 GB GDDR6
Virtualisation Workload Ultra-high-end rendering, simulation, and 3D design with NVIDIA vWS. AI, deep learning, and data science with NVIDIA vCS. Work with the largest, most complex RTX enabled rendering, 3D design, and creative applications, with NVIDIA vWS. Mid-range to high-end rendering, 3D design, and creative workflows with RTX applications and NVIDIA vWS. Entry-level 3D design and engineering workflows with Quadro vDWS. High-density, low-power GPU acceleration for knowledge workers with NVIDIA GRID software.
Usage vWS, vPC, vApp, vCS vWS, vPC, vApp, vCS vWS, vPC, vApp, vCS vWS, vPC, vApps

For full GPU specifications and to compare models please see our Professional GPU Buyers Guide.

Ways to buy and deploy Virtual Compute Server

There are a number of supported GPUs for vCS deployment, depending on what performance is required and how many GPUs are to be shared per virtual machine. vCS also supports NVIDIA NGC GPU-optimised software for deep learning, machine learning, and HPC. NGC software includes containers for the top AI and data science software, tuned, tested, and optimised by NVIDIA, as well as fully-tested containers for HPC applications and data analytics.

Regarding product licensing, unlike NVIDIA vPC and NVIDIA vDWS, vCS is not tied to a user with a display. It is licensed per GPU as a 1-year subscription with NVIDIA enterprise support included. This allows a number of compute workloads in multiple VMs to be run on a single GPU, maximising utilisation of resources and your investment. It is also possible to co-terminate new and existing licences - meaning that you reduce licensing admin by synchronising the renewal dates in line with latest purchases. With these hardware and licensing considerations in mind, there are three main routes to creating a vCS solution.

Purchase vCS licences

If the servers you have are equipped already with sufficient GPUs for your chosen performance requirement, then you just need to buy NVIDIA vCS licenses. Please contact us to discuss your NVIDIA vCS licensing requirements or click below.

Buy vCS Licences

Upgrade your servers and add licensing

If your servers are not equipped with the necessary supported GPUs but are capable of being upgraded, then this is a straight-forward option. Once this is done, in combination with the purchase of vCS licenses, you will be ready to deploy vCS. Please contact the Scan AI team to discuss your NVIDIA GPU and NVIDIA vCS licensing requirements.

Purchase new servers and add licensing

If your servers are too old, or would be too expensive to overhaul to the required GPU specification, then you need to invest in a purpose-built server or servers to deliver the vCS experience you want. Please contact the Scan AI team to discuss your project so we can spec-up suitable GPU-accelerated servers with the appropriate NVIDIA vCS licenses.

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