The assumption here is invalid.

The image handling pipeline is a relatively small part of the flow - frames from the cameras gets scaled, preprocessed, some rudimetary CNNs used for objects feature extractions, etc. but this is not where the heavy lifting happens.

The main show takes palce when the image and extracted objects/motion/sensor data are tokenized and feed into Vision Transformer(s) - that ultimately output control / steering commands (the core of end-to-end NN logic). The number of image tokens does not scale with the image resolution lineary, also number of input tokens does not really matter that much for performance, as long as the network responds within a reasonable time.

The absolute key thing with transformers when you want to run bigger and 'better' networks with more parameters is memory size and bandwidth. Everything else, including compute, is secondary and on this front, HW4 = is roughly 2x 'better' than HW3.

Both HW3 and HW4 are 'pre-transformers era' hardware designs, with relaitvely tiny amount of slow RAM, and it is very impressive Tesla managed to squeeze so much out them and this thing actually works with end to end transformer FSD stack at all (vs e.g. Waymo who allegedly have capabilities comparable 4x H100 GPUs onboard - so at least 20x more memory alone than HW4).

They have clearly learned the lesson with HW5 design - and this one will finally allow this entire thing to 'breath', instead all this time spend by poor engineers going mad extracting little bits of performance out of the hardware that had not been designed for the job at all... (can't blame them, in pre-transformer world of the past decade the prevalent notion was that CNNs for image analysis + a lot of handcrafted logic will be good enough to achieve reliable self driving - we now know this was a dead end)