Scan AI

NVIDIA Virtual Compute Server

Scan Cloud can deploy vCS and hardware to deliver AI, deep learning and HPC applications to any device, anywhere

Private Cloud Solutions - by Scan Cloud and NVIDIA

NVIDIA Virtual Compute Server (vCS) is a virtualisation solution that delivers high performance compute, simulation, deep learning and AI virtually, in a user experience that’s nearly indistinguishable from a physical server. vCS is deployed as a private cloud infrastructure with analytics and compute power delivered from centralised server hardware. The vCS solution includes comprehensive management and monitoring capabilities, so can scale and make the best use of your hardware resources.

As an NVIDIA Preferred Virtualisation partner, the Scan Cloud team is able to advise on all aspects of vCS including licences, the hardware required to deliver the platform as part of a fully deployed solution - 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.

 

NGC

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

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

FEATURE LIST
Features NVIDIA vCS
Windows OS Support  
Linux OS Support chart check
NVIDIA Compute Driver chart check
ECC reporting and handling chart check
Management and monitoring chart check
Multi-GPU Support chart check
Maximum Hardware Rendering 1X 4K
Maximum resolution 4096 x 2160
FEATURE LIST
Specification A100 V100S A40 RTX 8000 RTX 6000 T4
Memory 40GB
HBM2
32GB
HBM2
48GB
GDDR6
48GB
GDDR6
24GB
GDDR6
16GB
GDDR6
Peak FP32 19.5
TFLOPS
16.4
TFLOPS
38.1
TFLOPS
14.9
TFLOPS
14.9
TFLOPS
8.1
TFLOPS
Peak FP64 9.7
TFLOPS
8.2
TFLOPS
- - - -
NVLink number
of GPUs per VM
Up to 4 Up to 8 2 2 2 -
Multi-GPU per VM Up to 16 Up to 16 Up to 16 Up to 16 Up to 16 Up to 16

Ways to buy and deploy NVIDIA 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.

NVIDIA vCS is licensed per GPU, allowing multiple compute workloads in multiple VMs to be run on a single GPU. It is possible to co-terminate new and existing licences - meaning that you reduce licensing admin by synchronising the renewal dates in line with latest purchases.

We can provide a full deployment of hardware and software, fully configured to deploy your own private cloud servers.

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