A benchmarking script for AI video subnet jobs

Hi all,

Just some initial takeaways about GPU RAM.

It looks like if your card maxes out it’s dedicated RAM it will pull from your shared GPU RAM which is the RAM on your system.

Once it pulls RAM from your system is slows down the results dramatically. Between 14x and 102x.

DrewTTT’s RTX3080 (10GB) card was 67x slower than Papa Bears RTX4090 (24GB) on Image-to-Video because it borrowed 1.9GB allocated memory and 6.32 reserved memory from the system.

Also if the system RAM cannot share enough RAM to meet the reserved GPU RAM it just throws out of memory.

My prediction is that this increase in this rendering time will be too slow from our network. (unless people don’t mind if things take 1 week to render?)

Is there a way we can restrict the docker image or model to using dedicated memory only?