Yes, the more GPU cores (volume of work that can be done in parallel) the more memory needs to be allocated to allow that to happen.
Additionally to that, differences in VRAM usage on Apple Silicon machines is expected, and is based on the amount of memory the machine has, along with the generation of GPU. There’s an algorithm which looks to leverage more UMA to increase performance when the machine has it available. This algorithm could be refactored to consider the memory consumed by the asset itself, where an out of memory would otherwise occur.