I am curious as to why they would offload any AI tasks to another chip? I just did a super quick search for upscaling models on GitHub (https://github.com/marcan/cl-waifu2x/tree/master/models) and they are tiny as far as AI models go.
Its the rendering bit that takes all the complex maths, and if that is reduced, that would leave plenty of room for running a baby AI. Granted, the method I linked to was only doing 29k pixels per second, but they said they weren’t GPU optimized. (FSR4 is going to be fully GPU optimized, I am sure of it.)
If the rendered image is only 85% of a 4k image, that’s ~1.2 million pixels that need to be computed and it still seems plausible to keep everything on the GPU.
With all of that blurted out, is FSR4 AI going to be offloaded to something else? It seems like there would be a significant technical challenges in creating another data bus that would also have to sync with memory and the GPU for offloading AI compute at speeds that didn’t risk create additional lag. (I am just hypothesizing, btw.)
All good! It’s the same situation as I described and I see that increasing temps did help. It’s good to do a temperature tower test for quality and also a full speed test after that. After temperature calibration, print a square that is only 2 or 3 bottom layers that covers the entire bed at full speed or faster. (It’s essentially a combined adhesion/leveling/extrusion volume/z offset test, but you need to understand what you are looking at to see the issues separately.)
If you have extrusion problems, the layer line will start strong from the corners, get thin during the acceleration and may thicken up again at the bottom of the deceleration curve. A tiny bit of line width variation is normal, but full line separation needs attention.
Just be aware if you get caught in a loop of needing to keep bumping up temperatures as that starts to get into thermistor, heating element or even some mechanical issues/problems.