vScaler has rolled-out
native Virtual GPU (vGPU), empowering customers with the ability to split a
single physical GPU up in to a number of smaller virtual GPUs. This approach
offers greater cost-efficiency for those that don’t require the full power of a
dedicated GPU.
vGPU differs from PCI passthrough, which is also supported by vScaler, in
that PCI passthrough has typically only been suitable for workloads that can
saturate the full capability of the GPU (for example, high performance
computing, deep learning and machine learning algorithms). This advancement,
under vScaler, broadens the GPU capability with the hypervisor license-free
virtual machines vScaler provides.
vScaler’s Director of Cloud and Managed Services, Glenn Rosenberg comments
“vScaler was created to deliver a very high-performance experience to users.
vGPU enables the user to split out the immense performance of a NVIDIA GPU and
assign the level of resource needed for their application, allowing other users
access to the remaining GPU resources, maximizing the performance and utilization”.
“By hosting NVIDIA GPU solutions within its revolutionary cloud environment,
vScaler is actively helping to broaden GPU adoption from early stage
development platforms to large scale production environments”, comments Alan
Rogers, Enterprise Partner Business Manager, Northern Europe, NVIDIA. “With
support for containerized environments such as Docker and Kubernetes, and the
recent addition of vGPU support, vScaler speeds-up and simplifies the large
scale deployments of GPU-accelerated applications”.
The efficiency of vScaler with vGPU support greatly improves performance,
compared with traditional architectures, and allows organizations to build
virtual desktop infrastructures (or VDIs) that cost-effectively scale this
performance for the business.
IT administrators can manage resources centrally instead of supporting higher
cost physical workstations at every single desk. This virtualization technique
means the number of users can be scaled up and down based on project and needs.
With its HPC-on-Demand offering, vScaler has long supported deep learning and
machine learning research requirements, enabling users to spin up deep learning
clusters with the appropriate frameworks (eg. Tensorflow, Caffe, Theano)
installed and accelerated using the world’s fastest NVIDIA GPUs, purpose-built
to dramatically reduce training time for AI simulations.
Utilising the latest generation NVIDIA Tesla GPUs, vScaler provides the
performance and flexibility for the most complex AI and deep learning tasks,
including but not limited to medical imaging, genomics, bioInformatics,
autonomous driving and many more. Now with native vGPU, we can complement the
above workloads with high-end workstations, VDI and virtualized training
environments, paving the way for vScaler as the platform of choice for
‘automated everything ‘.
vScaler is currently offering a free trial
of its GPU in the cloud offering to customers that wish to try out GPU
technology before investing.
