clusterplex VS nvidia-patch

Clusterplex

I'm also very interested in distributing transcoding jobs via https://github.com/pabloromeo/clusterplex. Have you tried that yet?

Here is a link to one of the Cluster projects: https://github.com/pabloromeo/clusterplex

I use a forked version of this: https://github.com/pabloromeo/clusterplex which has been working awesome.

This is not true. You could setup Plex in a cluster and based on which content is requested let it handle by a certain transcoding worker. You have Clusterplex or you have UnicornTranscoder. Both are actively maintained repositories. This does however require some knowledge of Linux/Kubernetes so if OP (/u/slacktopuss) doesn't know how Ubuntu and Kubernetes works, it's going to take a long time before you're up and running.

And if UnicornTranscoder isn't it, you can always look intoClusterPlex's implementation. Basically the same as how UnicornTranscoder works (Node.JS as balancer and some remote FFMPEG installs).

To see full working examples of this we can take a look at the builds of two pet projects of mine: Docker-Dogecoin and ClusterPlex.

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