Data scientists and machine learning developers are building and training very large models these days with more extensive GPU memory needs. Many of these larger ML applications need more than one NVIDIA GPU device on the vSphere servers on which they operate or they may need to communicate between separate GPUs over the local network. This can be done for the purpose of expanding the overall GPU framebuffer memory capacity or for other reasons. Servers now exist on the market with eight or more physical GPUs in them and that number of GPUs per server will likely grow over time. With vSphere 8, you have the capability to add up to 8 virtual GPUs (vGPUs) to one VM.