Bundling my two sata ssds into a single zfs volume, instead of manually moving stuff around between the nvme and two sata ssds. Combined with compression, and for my code folder deduplication it also resulted in a lot more usable space.
“But throughout that time, I actually had no inkling what game development was actually like. How hard the designers, programmers, artists, producers, and everyone else worked,” he says. “The struggle to bring a vision to life with constantly shifting resources. The stress.”
Then tell us about it. Make it heard where you get Stressed and where you rub up on the state of the art. List off what had to be finished in crunch time. What got pushed by Marketing or Management. Leaving everything up to a nebulous “you don’t know” makes any criticism easily dismissed and reduces leverage against systemic issues.
how do you stop it on client side? I’m not sure if it has been deployed into the wild but these days computer vision is good enough to just work off the images. Capture image signal, fake usb mouse outputting movements calculated from image data. If this isn’t already available it’s only held back by the need for extra hardware.
Its highly dependent on implementation.
https://www.pugetsystems.com/labs/articles/stable-diffusion-performance-professional-gpus/
The experience on Linux is good (use docker otherwise python is dependency hell) but the basic torch based implementations (automatic, comfy) have bad performance. I have not managed to get shark to run on linux, the project is very windows focused and has no documentation for setup besides “run the installer”.
Basically all of the vram trickery in torch is dependent on xformers, which is low-level cuda code and therefore does not work on amd. And has a running project to port it, but it’s currently to incomplete to work.