Looking Glass is kind of the closest you'll get for a trivial "I want shared GPU virtualization on my workstation", but GPU partitioning doesn't really work tha

Looking Glass is kind of the closest you'll get for a trivial "I want shared GPU virtualization on my workstation", but GPU partitioning doesn't really work that way. Outside of the baseline support in the hardware itself, which is nowhere near generic enough for "works on any GPU" (it took supervisory frameworks to even get CUDA/OpenCL/etc to a point where you can stop worrying about writing transforms from scratch and just let PyTorch abstract it a little), this model of GPU partitioning doesn't perform well. How do you allocate vGPU memory between a ML/AI VM, a VDI VM, and a gaming/CAD VM? All have dramatically different requirements. You also can't think of shader/GPU cores in any way similar to CPU cores. They're essentially just vector/linear algebra accelerators with little to no branch prediction, speculative execution, or anything else you'd expect. Otherwise, you can sort of follow along here: https://openmdev.io/index.php/GPU_Support https://openmdev.io/index.php/GPU_Support There's an effort, but it's far from where you want, and there's no indication it will get there unless you can get all the vendors to agree on a standard at some point in the future.