Advantages of unified (shared) memory are avoided VRAM size limit and faster bandwidth for interaction involving both RAM- and VRAM-like memory use. An advantage of a separate dGPU can be availability of CUDA, RT and Tensor cores. Whether AI inference profits from a particular system, memory, GPU and possibly networking structure and from the adforementioned particular types of cores depends on the software code and algorithms. Avoiding unified memory might, or might not, accelerate different softwares of AI inference. Adding (and licensing) CUDA, RT and Tensor cores would greatly accelerate quite a few softwares of AI inference - those currently profiting from Nvidia dGPUs. If Apple really wants to go AI with M5, it must offer those types of cores together with equivalents of CUDA, CuDNN and TensorRT libraries of comparable algorithmic quality because this alone is responsible for up to the speed factor 2.95. However, Apple hating license fees is unlikely to go all in at AI and rather will try to let PR replace much of then still missing actual speed. With instead reinventing AI-specific cores and libraries for them, Apple will need 20 years to catch up to Nvidia's AI capabilities and then AI softwares must be rewritten for Apple silicon.