Most edge AI software is still written as if the accelerator lives on the other side of a bus. Tensors get staged, copied, and copied back. Buffers get duplicated because two frameworks each insist on owning them. The memory budget gets split into a CPU half and a GPU half before a single model is loaded. On a part where the CPU, GPU and NPU share one physical memory pool, those habits are pure overhead, and they are baked into most of the stacks people port over from discrete-GPU development. This session is a practical guide to the memory model that Physical AI workloads need on a unified-memory edge SoC, using ROCm on an integrated GPU as the working example. It covers where zero-copy actually happens versus where the runtime quietly inserts a copy, how to allocate so that a multi-gigabyte model and a live camera pipeline coexist without fragmentation, what the NPU and the iGPU can and cannot hand to each other, and how to tell a real memory win from a measurement artifact. We show a 7B-class VLM, a perception stack and a control policy co-resident in one shared pool, with measured results.