By Anne Hecht, NVIDIA Senior Director of Product Marketing, NVIDIA GRID

Virtual GPUs Empower Enterprise Performance, Security and Productivity

From AI to visualization and VFX, keeping up with today's increasingly complex workloads can be a challenge for IT teams, especially as graphics-intensive use cases continue to grow alongside the need for greater data security. GPU-accelerated virtualization is helping enterprises stay on top of these challenges.

More enterprises are turning to GPU-accelerated virtualization to give users greater mobility and flexibility to get their work done wherever they go, while also meeting enterprise goals for agility, efficiency, and data protection. Below are key trends we can expect to see in 2020 as more businesses deploy GPUs in their virtual environments:

Agility, Access, and Affordability Expand with GPU Sharing in the Cloud

Cloud-based solutions for enhanced performance and flexibility are ubiquitous in the enterprise. With virtual GPU solutions in the cloud, companies will be able to share GPU resources from the cloud - so multiple virtual machines can be powered by a single GPU, or multiple GPUs can power a virtual machine to maximize utilization and affordability. The power and performance of GPU-enabled virtual workstations will help enterprises provide employees with far better cloud computing experiences that allow professionals to float more of their most intensive workloads up into the cloud with ease, even when working in AI, deep learning, data science and computer graphics.

Increased Security Leads to Enhanced Accessibility

As businesses protect themselves against cyber threats and data breaches, more enterprises are turning to virtual GPU solutions to make peak performance possible on any device, while keeping information safely hosted in the central data center. Companies like Honda are choosing virtualization to take advantage of better application performance, faster access to data and enhanced security. Virtual GPUs enable professionals to collaborate on large datasets, and expand virtualization to more users with secure access to larger files and graphics-intensive 3D applications. Given that the data sets and models of virtual workstation users can easily surpass hundreds of megabytes to even terabytes in AI, deep learning, and data science use cases, virtualization with vGPU provides the data proximity needed to ensure both enterprise security and productivity.

Server Virtualization & Containers Get Precision Acceleration

AI, deep learning and data science workloads can now be easily deployed in virtualized environments with GPUs. The benefits of server virtualization were previously only possible on the CPUs. With new solutions like NVIDIA vComputeServer, IT admins will better streamline management of GPU-accelerated virtualized servers while retaining existing workflows and lowering overall operational costs. With a vast selection of containers and pre-trained models that are GPU optimized available through NVIDIA's NGC software hub, compute-intensive workloads can now be deployed quickly and easily in VMs. Container adoption is continuing to grow, and with vComputeServer, IT can rest assured that their virtual infrastructure will be able to deliver the performance needed for even their most data-intensive use cases.

Better User Experience Gets IT More Gold Stars

Better user experiences drives productivity, and with GPUs, profile management becomes much easier. With GPU-enabled virtual workstations, enterprises will gain greater flexibility because they can scale up and down as business needs change.

Advanced benchmarking tools like NVIDIA nVector measuring key aspects of the user experience, such as end user latency, frame rate, image quality and GPU utilization will provide businesses better insight into the actual end user experience. This enables IT to size the VDI infrastructure based on utilization thresholds that are relevant to users and the business. And of course, better user experiences mean fewer IT support calls, and more five-star experience reviews.

5G Pushes Virtualization to the Edge

With the rise of 5G, more data will be accelerated by GPUs at the edge, opening up new use cases for virtualization. Solutions like NVIDIA CloudXR will help telcos, software makers and device manufacturers leverage the speed and mobility of 5G signals to provide low-latency experiences to millions of customers in more locations than previously possible. Retailers and service providers will be able to add Conversational AI into their storefronts with loyalty programs that can remember customer preferences, and check them out with new, secure ways to process financial transactions at the edge. Companies can deliver content from 5G networks to any device, and virtual GPUs will provide the performance needed to deliver these experiences quickly for these complex new workloads.

For those of us working in technology, 2020 is not only a brave new year, but also the start of an exciting new decade of innovation. From powering graphics-intensive workloads, AI, Machine Learning, data science, and bold new 5G experiences, virtual GPU acceleration will be a cornerstone for performance, agility, and security. With the world looking for ways to grow efficiency and access to technology, GPU virtualization will help many more innovators make their dreams a reality.

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About the Author

Anne Hecht, NVIDIA Senior Director of Product Marketing, NVIDIA GRID

Anne is the Sr. Director Product Marketing for NVIDIA GRID where she oversees outbound marketing activities for the virtualization business. With more than 25 years of marketing experience, Anne has held senior positions at ForgeRock, Agari, NComputing and Sun Microsystems. She holds an MBA from the Wharton School of Business at the University of Pennsylvania and a bachelor's degree from the University of Pennsylvania.