GPU virtualization for every workload - from VDI to AI
GPU-accelerated applications are used to
- Determine better and faster business decisions,
- Increase productivity,
- Develop better products that can be brought to market faster.
NVIDIA® virtual GPUs (vGPUs) enable enterprises to access powerful GPU performance in the enterprise data center to support all workloads, from virtual client computing to compute-intensive workloads such as AI, data science, and high-performance computing (HPC).
WHY NVIDIA Virtual GPUs?
First class user experience
Ultimate user experience and the ability to support compute and graphics workloads in hypervisor-based virtualization environments
Calculable performance and nearlyBare METAL PERFORMANCE
Consistent performance and quality of service, both on-premises and in the cloud. Performance is virtually indistinguishable from that of a bare metal environment.
Constant innovations
Regular software releases ensure that you are up to date with the latest features and improvements.
Optimal user density
Industry's highest user density solution with support for up to 32 virtual desktops per physical GPU Lower total cost of ownership with more than eight vGPU profiles for maximum flexibility to match resource allocation to your users' needs
Optimal management and monitoring
True, complete management and monitoring with real-time insight into GPU performance and migration technology, plus comprehensive partner integrations that let you use your familiar and trusted tools
Most comprehensive ecosystem support
Support for all major hypervisors, more than 140 certified servers and an extensive portfolio of professional applications accelerated by GPUs.
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NVIDIA RTX virtual workstation performance
GPU accelerated data science platform
The advantages of the GPU come into play especially when - as is typically the case with analytics or AI applications - the workloads can be well parallelized so that they can be processed on multiple GPUs. With the RAPIDS data science platform, NVIDIA gives researchers and engineers a powerful tool to quickly analyze even very large data sets and make accurate business predictions at extremely high speeds. The open-source RAPIDS solution makes it easy to tackle even the most challenging tasks, such as predicting credit card fraud probabilities or consumers' expected purchasing behavior.
Accelerate AI models in the data center with RAPIDS
The RAPIDS platform integrates with the many data science libraries and workflows. It provides a set of open-source libraries for GPU-accelerated analytics applications, machine learning, as well as data visualization. This gives scientists the opportunity to manage the entire data science pipeline using GPUs. Overall, they can significantly reduce the time spans of their workouts - from days to hours and from hours to minutes, depending on the data set.
NVIDIA GPU Cloud
GPU-accelerated cloud computing thus becomes available on demand on all major cloud platforms.
this is because these NGC containers can use the same stack regardless of the cloud in which they are deployed. They are optimized, tested and certified by NVIDIA for use on DGX systems, selected TITAN as well as Quadro GPUs and NVIDIA GPUs in the cloud (Alibaba, Amazon, Google, Microsoft and Oracle). This eliminates the complicated, time-consuming software integration that users previously had to manage on their own.
DISCOVER VIRTUAL GPU SOFTWARE SOLUTIONS
NVIDIA Virtual Compute Server (vCS)
Accelerate virtualized compute workloads - from AI and machine learning to data science.
NVIDIA Virtual PC (vPC)
Virtual Desktop (VDI) for Knowledge Workers using productivity applications for Office & Multimedia.
NVIDIA RTX Virtual Workstations (vWS)
Virtual workstations for creative and technical professionals using graphics applications.
NVIDIA Virtual Applications (vApps)
Application streaming with Remote Desktop Session Host (RDSH) solutions.
Added value for IT professionals - unsurpassed
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Reduce IT costs and increase business agility with vGPU tools for live migration and management and monitoring.
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Run even the most demanding workloads with dramatically accelerated performance by connecting multiple NVIDIA vGPUs together.
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Use GPU-accelerated computing resources to run mixed workloads to increase productivity and utilization while reducing costs.