vgpu_unlock VS nvidia-patch

This isn’t even the first time a hacked driver has been used to unlock some HW feature - https://github.com/DualCoder/vgpu_unlock

Alternatively enable vGPU for the 2070 and use it for both Jellyfin LXC and Windows VM. https://github.com/DualCoder/vgpu_unlock

I'm on Linux and I'm running a 3070 Ti (Nvidia). I have always wanted to do GPU virtualization but because NVIDIA won't release vGPU for consumer card no one can do it without crossing legal red tape or problems with bricking your GPU. I did find this [https://github.com/jamesstringerparsec/Easy-GPU-PV] however it is only for windows, I found this [https://github.com/Arc-Compute/LibVF.IO/] and does not work with my GPU, and this [https://github.com/DualCoder/vgpu_unlock] and can't get it to work. Done any one know an alternative on Linux that work just like this, overcoming these problems (on KVM)?

Otherwise, your only other option is the real hardware virtualization options that are available. NVIDIA's enterprise vGPU solution is for expensive compute cards however some have had good luck making vGPUs work on consumer NVIDIA cards with tools such as vgpu_unlock

There was a hack to enable it on some consumer cards, but it’s not available on Ampere/30x0 cards: https://github.com/DualCoder/vgpu_unlock/issues/8

Pci passthrough https://github.com/mbilker/vgpu_unlock-rs https://github.com/DualCoder/vgpu_unlock https://github.com/DualCoder/vgpu_unlock/issues/91 https://gitlab.com/polloloco/vgpu-proxmox https://github.com/joeknock90/Single-GPU-Passthrough https://gitlab.com/YuriAlek/vfio#start-here https://www.reddit.com/r/homelab/comments/b5xpua/the_ultimate_beginners_guide_to_gpu_passthrough/ https://forum.level1techs.com/t/single-gpu-passthrough-with-proxmox/113282/2 https://forum.proxmox.com/threads/problem-with-gpu-passthrough.55918/

This can be patched out. https://github.com/keylase/nvidia-patch

NVIDIA Corporation GA106 [GeForce RTX 3060] and I applied the patch here https://github.com/keylase/nvidia-patch

> CUDA, and pretty much all optimization(hacks) done to run games better

And arbitrary limitations implemented at the driver level to force you to purchase their enterprise GPUs, see https://github.com/keylase/nvidia-patch#nvenc-and-nvfbc-patc...

Nvidia has no motivation to make a consumer card with lots of VRAM, that's basically the only (relevant) separator between the GeForce family and the Quadro lineup.

There are restrictions on NVENC streams with consumer cards, but that has been a solved problem for a while [0].

If they were to make a consumer card with more VRAM, it would immediately undercut their own Quadro/Tesla lineup, which cost substantially more. I don't see a reason for them to do it.

0: https://github.com/keylase/nvidia-patch

I have never had to do the session limit bump thing from the last link. I have a 3090 as well and simply did the initial unlock, which worked fine. I would reinstall fresh drivers from Nvidia, making sure you install the newest one that is supported by the unlock tool (536.40 as of this post, the GitHub for the patch has links to the drivers - https://github.com/keylase/nvidia-patch/tree/master/win)

Aren't these Quadro versions. The patch here. https://github.com/keylase/nvidia-patch supports Quadro versions of you click on the win clickable.

Second, the GPU. The GPU is probably as important as the CPU, and in some cases more important, and when we talk about GPUs we will primarily talk about Nvidia GPUs as they are officialy supported by the Plex team. NVIDIA GPUs are important for Plex hardware transcoding due to their dedicated video encoding/decoding units, superior performance, wide codec support, improved video quality, reduced CPU load, power efficiency. They offer a powerful hardware acceleration solution that can greatly enhance the transcoding capabilities of a Plex server. It's also important to note that Nvidia GPUs require a patch to unlock the number of HW transcoding streams. Dedicated GPUs are large pieces of hardware and have their place in desktop PCs. However, they can also be used with mini-PCs by using an external GPU enclosure.

As there's no NVENC patch available (yet) for the Beta driver branch - referring to this one: https://github.com/keylase/nvidia-patch - which can lift the limits of HW transcoding, I was now wondering a little, as I can see 5 (hw) streams on Plex, which actually shouldn't/cannot be the case no?

I haven’t done this yet but there is a patch on GitHub that removes the limitation for consumer GPUs. Makes lower end cards more attractive for this type of work