GPU compatibility on Virtual machine

Hi, I’m new to Salad.

I downloaded the app on Windows 10 VMware Workstation 16 Player (Non-commercial use).
I wanna use the app on this machine instead of my main machine. It works great on the CPU, but not on the GPU.
I enabled Accelerate 3D grahics and the graphics memory is 1GB. I also installed VMware tools on the Virtual Machine.
When I try to use the GPU instead of the CPU it doesn’t work (even if I choose Override Compatibility Detection).
I know it’s not that critical, and I can installl it on my main machine or use the CPU, but I would like to have the ability to use the GPU on my VM.

Sorry for my English.

Edit: I forgot to mention, it doesn’t even start the mining (when I try to use the GPU).

The miner running in VMware is seeing a fake/virtual GPU. VMware Workstation translates any calls to the fake GPU to the real, underlying GPU. This works fine for YouTube and some games, but it doesn’t work for miners. The miners are highly specialized applications that know how to use unique AMD and NVIDIA features to maximize performance. The VMware translation layer means the miner can’t talk directly to the GPU and leverage all of it’s capabilities. As a result, the miner developers don’t “waste time” trying to program their miners to work with a fake GPU.

You need to either GPU mine on the host OS, or you need to find a way to pass-through the GPU to the guest OS without it being virtualized (wrapped by the fake adapter). You’ve got the right setup if, inside the VM, you see an AMD or NVIDIA GPU in the hardware manager and if you have to install their drivers.

I’m not sure if or how this can be done with VMware Workstation, but I do know that other Chefs have accomplished this in the past with other VM platforms. If you do get it working, it would be awesome if you contributed to a community guide so others could learn from it, as well.

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Thank you so much, I downloaded the application on my host OS after I saw that the app is open source.
The app works great and a lot faster.
Thanks again.