I was in a similar boat, and ended up buying a used convertible tablet from eBay instead. Much more Linux friendly, 12” Toshiba Dynabook. Might be a better option.

Yes, but it’ll likely still be faster, just not as dramatically. Half of 4-94x is still 2-47x faster.

http://vgmusic.com/

Where do you live, Alberta? Or one of the maritimes??

3-4 days easy. That’s with turning on all the gps/heartrate/iOS sync functions tho. Turning off the gps and heart rate monitor and I’m sure I could get 7 days easy, and turning fewer notifications I have no doubt it’s go 2weeks.

My bad it’s actually https://banglejs.com/

Tried https://bangle.js? Loving mine so far.

“Both sides”

“Vote third party!”

Wtf seriously this isn’t the same thing remotely but the arguments used are.

Where has that been all my life!

I’ve always found the documentation around virtio-GPU and virtgl very lacking, and have never gotten them working. Would love to get pointers if anyone has a good source.

I don’t see any performance differences with the vgpu actually. I have more performance bottlenecks with the CPU, and my RAM isn’t the fastest, so I think I’m more CPU limited. Benchmarks I have run that are GPU focused seem to show little to no difference from what the physical card would do.

Yeah unfortunately. 20xx is last generation supported so far via the patch, not sure if support for later cards is coming or not.

No, but I think you’d have some problems. Only the host has access to the actual DisplayPort outputs, all the vgpus have virtual displays, I don’t think there’s a way to make them use the physical out.

Sure, but you’ll get diminishing returns most likely as consumer hardware doesn’t really have the resources to scale that way very well if all the VMs are running demanding apps simultaneously.

Even for something like 4 VMs that just do NVenc, there are limits for how many streams the GPU can do. I think there’s another patch that lets you raise that, but at some point you’ll run out of resources quick. Even powerful consumer gear isn’t really designed to be used by more than one user/app and it starts to show the more you virtualize and split those resources.

I’ve been doing exactly that at home for a couple years now. First with Parsec, now Sunshine/Moonlight.

Host is Proxmox on Ryzen 5800x, 64gm RAM GPU is 2070 Super, with VGPU patched drivers from https://gitlab.com/polloloco/vgpu-proxmox

When I’m gaming I’ll dedicate the full 8Gb to my windows Vm, otherwise I split it in 2 or 4Gb chunks to Jellyfin or my home camera monitoring. 8gb can’t split very many ways, and most things require at least 2 to run.

Locally at home I can run 1440p 60fps rock solid over wifi on any device, from my phone/old laptop/apple tv/raspberry pi. Remote I can do 1080p60, but a bit more hit or miss depending on my network connection.

Experimenting with LLMs I’ve done through the same windows VM, or to a ubuntu dev VM. Works the same way. I’m thinking of transitioning my gaming VM to Linux too.

The amount of VRAM is the hard limitation to get past, the virtualization tech itself has been there for a while.

But to be perfectly honest……it really was just a “let’s see if I could do this” type task, direct GPU pass though is more straightforward and it’s not really worth splitting 8Gb these days. Unless you get a card with significantly more VRAM passthrough is much less work.

I suspect we got off on the wrong foot when I called it a product instead of just a project idea, but I don’t actually disagree with that stance. Cheers.

That’s the act I was thinking of. I’m almost certain web streamers will say it doesn’t apply to them……and it’s definitely one of those things you’d take for granted until you hit the “new” streaming medium and realize why it was necessary in the first place.

And you came here with nothing productive to add 🤣 we can both make useless comments it seems

Yeah I think hdcp and reprocessing would be most difficult. There are hdmi splitter devices like those used for coloured bias lighting that I think could be used….similarly I think the processing actually isn’t unsolvable, it’s not much different than object detection from a live camera stream. I agree re-encoding the stream would be too hardware intensive for anything “cheap” like a pi, hence the secondary device control alternative initially, but analyzing the stream should be possible.

Because I think this could be neat product……kinda like PiKVM, but maybe using ML to detect ads and make it a nice community tool to block in a device independent way. like hardware Adblock.