Balance is the key to an efficient system. Too little storage may not give you the space for critical applications. And too much power can cause you to waste money on energy you don’t need. The same goes for processors, RAM, network adapters, etc. When you don’t align your hardware with your needs, you’re going to run into performance issues. Virtualization is no different. Whether virtualized or running on bare metal hardware, applications have similar needs. For example, software that powers gaming, simulation, and video and photo editing are all processor intensive. Running these types of applications solely on CPUs will give you poor performance at best. Using GPU virtualization can make your machines much more efficient. For example, when GPU virtualization is used for specific applications, it can cut CPU usage by two-thirds. What is GPU virtualization? It’s when you take physical GPUs and use them to render graphics for your virtual machines – similar to virtualizing other components like storage and CPUs. Let’s take a look at how GPU virtualization is being used today.
Enhance User Experience With GPU VirtualizationUser experience is a key indicator of how well your IT infrastructure is working. When applications are sluggish, or you experience bottlenecks due to unoptimized systems, it means you need to adjust your computing resources. One such adjustment is to balance workloads using vGPU resources. Here are three use cases for GPU virtualization:
- Boost the performance of virtual machines. Non-GPU virtual machines are limited in parallel processing due to the insufficient number of cores in CPUs. In contrast, GPUs have thousands of cores that speed up tasks in AI, ML, gaming, and other compute-intensive use cases.
- Reduce system bottlenecks. One of the uses of virtualization in business is to make computing resources and data available to users. A system that only uses CPUs will get bogged down during peak workloads. Using GPUs and CPUs together allows virtual machines to distribute workloads better and maintain strong performance.
- Enable applications that rely on GPU capabilities. Cloud gaming, AI software, and desktop virtualization are all applications that depend on GPU hardware to run smoothly. In gaming, lag makes or breaks the user experience. And with AI, slow computation can be the difference between running ML models in hours or days. Speeding up these tasks makes working in the cloud viable.