The vGPU method also provides full functionality, but it ensures that each virtual machine has access to a certain amount of the underlying GPU resources. With the second option of shared vGPU, the GPU capacity is split into multiple virtualized instances that can be attached to multiple virtual machines. Organizations typically use pass-through mode for specific workloads that require more dedicated capacity. This can also mean that resources are not being used most efficiently. However, this approach does not provide scale because the GPU cards are locked to one virtual machine. Pass-through mode provides full graphics compatibility, which means the end user can access the full functionality of the GPU attached to the machine. The main differences between these three delivery models are scale and compatibility with the different GPU capabilities to the virtual machine. Technologies that use this method include Nvidia vGPU and AMD MxGPU. GPU-based virtualization, having virtual GPU profiles attached to each virtual machine.

Technologies that use this method include VMware vSGA and the previous feature in Hyper-v RemoteFX vGPU. Hypervisor-based sharing of GPUs to virtual machines. Technologies that use this method include VMware DirectPath I/O, XenServer GPU Passthrough and Hyper-V Discrete Device Assignment. Mapping a physical GPU card directly to a virtual machine through the hypervisor. These GPUs are also the preferred choice of hardware to accelerate computational workloads in modern high-performance computing-based offerings.įor remote visualization workloads on traditional hypervisors, there are three options available to provide GPU capabilities to a virtual machine: These offerings provide a GPU-based desktop or application to remote end users.ĪMD and Nvidia have been working on GPU cards specifically suited for AI, and deep learning-based workloads such as the popular machine learning engine Tensorflow. How do different GPU offerings work?įrom a virtualization perspective, GPU-based offerings are primarily aimed at remote visualization and encoding. Nvidia and what kind of workloads they are best suited for. IT departments should educate themselves on the technical differences of AMD vs. Amazon and Google's cloud services have roughly the same amount of options as well. In Azure alone, Microsoft now has seven different virtual machines instance types with different GPU cards from AMD and Nvidia. At Microsoft Ignite 2019, Microsoft revealed that it was working with semiconductor vendor AMD to provide a new set of virtual machines on Azure powered by AMD-based GPUs.

Amd vs nvidia graphics cards comparison