UPDATE: I've had to rethink this substantially - the final product will be different, but viable. I'm attempting upscaling now directly on the V100 in WDDM mode, and that'll pipe through the igpu. Should mostly kill latency but will be complicated. This post still serves as a proof of concept, and I'll continue development and updating. The goalpost has shifted towards a true temporal upscaler directly on the WDDM card that takes advantage of its capacity. The goal is to get a high performance upscaler taking advantage of tensor cores to run natively on either a v100 16gb or a titan v. In theory, a V100 card absolutely blows the rtx 20xx line out of the water in both memory bandwidth (huge for ML speed) and raw compute, but the difficulty is going to be getting it to work, and actually compete.
I am going to continue to add development updates to this post until it's time for a new thread
Disclaimer: I'm still working on holding framerate always above 60 and still improving the upscaling quality (playing around with a bunch of different neural net implementations) - so take this as a working demo to back up the rest of this post and that I'm not just talking out my ass. I've only been working on this for a few days and have a long way to go. I think reddit is cranking down the res, so the youtube video is at https://www.youtube.com/watch?v=6-y3H98Y7hg&feature=youtu.be
TL-DR: I've developed a way to use an old, cheap nvidia datacenter card to run machine learning accelerated upscaling on any game you already own, without replacing your GPU or your setup.
Hi everyone,
I'm a software developer who's currently focused on AI/ML workloads. I tend to be thrifty and use hardware that's a little less mainstream to get extra performance.
I own a Tesla V100 - a nvidia chip from 2017/2018 that was the first chip to ever include tensor cores. The 16gb PCIe edition is currently $230 on ebay. I only own it in the first place for running and training LLMs.
I'm developing an AMD-FSR esque temporal upscaling solution that uses a small neural network on the V100's. I actually already have a working (alpha, buggy of course) implementation running. This began as a personal hobbyist project, as I actually own TWO v100s - I wanted to see if I could put the extra one to work doing upscaling while the main one does the gaming workflow. The way I have the display setup is funky and hacky - one v100 has had its driver modified to enable wddm mode, and I stream the output from that through an iGPU.
The realization I've come to is that what I'm developing should already work as a drop in with any video card, provided you have an extra PCIe x16 lane on your machine that's not in use. Essentially, the V100 intercepts the frames being rendered in real time, upscales them, and hands them back off to your main GPU.
I feel fairly confident there'll be interest in a fully developed version of this, considering any card that actually has AI upscaling is still crazy expensive. As I mention, a 16gb V100 can be bought for about $230 and dropped into any existing rig. This should also work with any existing graphics card you already have installed.
I'm not ready to release this yet, just gauging interest in a fully developed version. I'd be remiss if I only told you about it though, so here's an early demo of upscaling tomb raider 2013 720p to 1080p.