A virtual graphics processing unit (GPU) is a computer processor that renders images on a virtual machine (VM) host server and not on a physical end device.
Rendering and serving complex graphics with sufficient performance to endpoints in virtual and remote desktop environments is a challenge. Standard GPUs were originally designed to offload processing calculations from the CPU for graphics-intensive applications. To solve this problem, NVIDIA introduced the first virtual GPU in 2012, reducing latency and providing PC-like performance when rendering graphics to remote users. This is especially useful for users who need computer-aided design or 3D graphics applications.
How does virtual graphics processing unit technology work?
In a VDI environment that supports vGPU, the graphics card driver is installed in the virtualization layer along with the hypervisor. Then, depending on the type of graphics card you install, your virtual machines will be able to use graphics processing cores in the form of various profiles and in this way benefit from high graphics processing power.
Also, with the help of this technology, it is possible to share the processing resources of a graphics card for a number of virtual machines.
Since the work that is normally done by the CPU is offloaded to the GPU, both the processing power of the CPU is freed and the user will have a much better experience when using graphics software.
Advantages and features:
Savings in hardware supply costs
By implementing this technology, there is no need to provide separate hardware for each user. Rather, users can connect to their virtual desktop with a very cheap Zero Client and perform graphic operations with the high processing power that vGPU technology brings.
Centralized management of graphics processing resources
Through the software facilities that hypervisors provide for vGPU technology, it has been possible for the system administrator to easily allocate graphics processing resources to different virtual machines. You can also monitor their usage at any time.
Reduce CPU workload
With the addition of a graphics card to a server and the possibility of sharing it for virtual machines, many graphics processes are now performed by the cores of the graphics card. This leads to a reduction in CPU workload and this saved power can be used for other virtual machines.
Scalability by increasing graphics cards or servers
The licensed Nvidia’s vGPU technology has the ability to provide more graphics processing power for your work set by increasing the number of graphics cards or increasing the number of servers. In this model, you can benefit from this feature by maintaining the current infrastructure.
The possibility of accessing the virtual desktop through different devices
In the implementation with Nvidia VGPU, users including Zero Clients, PCs, tablets and mobile phones can easily access their virtual machines through the web or through the relevant Agent. In this way, they can use all kinds of processing resources, including graphics cards.
Under the hood
In a virtualized environment running NVIDIA Virtual GPU, the licensed NVIDIA Virtual GPU (vGPU) software is installed in the virtualization layer along with the hypervisor. The software creates a virtual GPU on which all virtual machines (VMs) can share the physical GPU installed on the server. For more demanding workflows, you can leverage the power of multiple physical GPUs in a single VM.
Our software includes graphics or compute drivers for each VM. The work normally done by the CPU is moved to the GPU, giving the user a much better experience. It can support compute-intensive workloads such as AI and data science, as well as demanding technical and creative applications in a virtualized cloud environment.
NVIDIA VGPU provides a great experience for all users.
The graphics requirements of business users are increasing. According to a white paper from Lakeside Software, Inc., Windows 10 requires 32% more CPU resources than Windows 7. Updated versions of major office productivity applications such as Chrome, Skype and Microsoft Office require much higher levels of computer graphics than ever before.
This trend toward digital advancement and graphics-intensive jobs will only accelerate. As CPU-only virtualization environments fail to meet the needs of knowledge workers, GPU-accelerated performance with NVIDIA has become a key requirement for businesses using virtualized digital workspaces and Windows 10.
How vGPUs Simplify IT Management
VDI allows IT administrators to centrally manage resources instead of supporting individual workstations at each employee’s location. You can also increase or decrease the number of users depending on your project and application requirements. The licensed NVIDIA Virtual GPU Monitoring gives IT departments the tools and insights to spend less time troubleshooting and more time on strategic projects. IT administrators have visibility into their infrastructure down to the application level, so they can identify problems before they start. This can reduce the number of tickets and escalations and reduce the time it takes to resolve issues.
VDI also enables IT to better understand user needs and adjust resource allocation. This reduces operating costs and provides a better user experience. In addition, NVIDIA’s Live Copy feature enables GPU-accelerated virtual machines to perform critical IT services such as workload balancing, infrastructure stability, and server software upgrades without virtual machine disruption. This enables IT to deliver high-quality user experiences with high availability.
License for NVIDIA vGPU
The licensed NVIDIA vGPU implementations require a license. Full-capability VGPUs are enabled when booting with a supported GPU. Unless the system is licensed, vGPU performance/features will gradually degrade over time as restrictions are applied.
Unlicensed vGPU performance/features are limited to:
- The frame rate is limited to 3 frames per second.
- GPU resource allocation is limited and some applications do not run properly.
- CUDA is disabled on vGPUs that support CUDA.
You can simply order and buy your suitable license and obtain the maximum performance of Nvidia VGPU.
Containers for AI and HPC that are GPU-accelerated.
For deep learning and high-performance computing, OVH and NVIDIA have teamed up to create the best GPU acceleration platform.
In order to provide a comprehensive catalog of GPU-accelerated containers that can be deployed and maintained for artificial intelligence applications, OVH’s NVIDIA GPU Cloud (NGC) combines the adaptability of the Public Cloud with the strength of the NVIDIA Tesla V100 graphics card.
It enables users to manage their projects on a trustworthy and effective platform that respects confidentiality, reversibility, and transparency.
A wide range of licensed Nvidia GPU software tools that are optimized for deep learning and high-performance computing (HPC) and that fully utilize NVIDIA hardware are readily available to researchers and data scientists through licensed NVIDIA GPU Cloud (NGC).
The most well-liked deep learning frameworks are supported by NVIDIA containers that have been optimized, tested, certified, and maintained in the NGC Container Registry. Additionally, it provides partner applications, third-party managed HPC application containers, and NVIDIA HPC visualisation containers.
A high level of availability and performance.
Resources that are always guaranteed and never over-allocated.
The OVH Control Panel lets you decide if you need a single server or a full infrastructure. With options for hourly or monthly billing for each server, billing is clear and straightforward.
When necessary, modify your instances to suit your needs by adding disks and IP addresses or shifting them from one instance to another.
Your cloud environments can be easily migrated and securely connected to products from other cloud providers thanks to OVH’s use of, contributions to, and support for OpenStack APIs.