Put your data center to work with GPU compute power!

This article details the configuration and deployment process for vGPU in a VMware Horizon environment. First, disable cluster DRS/DPM to prevent migration deadlocks. Then, add Q/C series vGPU devices that support CUDA to the virtual machine in VCenter. When installing GRID drivers on the Linux client, you must use deb/rpm packages instead of the runfile. Configure `gridd.conf` and the license Token, and verify the authorization status using `nvidia-smi`. When installing CUDA, strictly avoid using the system package manager. You must install it via the runfile and uncheck `cuda-driver` to prevent overwriting the GRID drivers. Finally, manually configure the PATH environment variable. To ensure cross-host migration capability, plan the GPU memory splitting scheme in advance (such as 1/8 or 1/3 splits), enable the `vGPU.hotmigrate.enabled` option, and ensure that BIOS settings on all hosts (such as ECC, IO MMU, and SR-IOV) remain consistent. After configuration is complete, you can install cuDNN for AI model training or utilize `h264_nvenc` for video hardware-accelerated encoding. Additionally, the article introduces methods for monitoring temperature via `nvidia-smi` to optimize fan noise, and provides a troubleshooting guide for driver version conflicts, license expiration, and BIOS configuration errors, aiming to help users build a stable and efficient GPU computing environment.