A shared pool can hold model weights and working data where the CPU and GPU can both reach them. This reduces explicit transfers and can let a local model use more total memory than a discrete accelerator with a smaller dedicated allocation.
Capacity alone does not guarantee speed. Memory bandwidth, contention, model format and software optimization determine how effectively inference uses the shared design.
ELI5
Unified memory architecture lets different processors in one computer use a shared pool of memory instead of keeping completely separate copies. This can reduce data movement and let a workload use more of the machine's total available memory.
For example, a local AI model may be too large for a graphics processor's separate memory but fit when the central and graphics processors share one larger pool. Actual speed still depends on memory bandwidth, processor performance, software support, and the model format.
Why can unified memory help local AI?
It gives the accelerator access to a larger shared pool and reduces some transfers between separate memory systems.
Does unified memory make every model fast?
No. Bandwidth, model architecture, precision and runtime optimization still control practical speed.
