AI Laptops Compared by Local AI Capability

What a laptop gives up

A laptop trades sustained power for portability, and local AI is a sustained workload. The same silicon that holds a steady clock in a desktop chassis will throttle in a thin one, so a laptop's quoted peak figures are a ceiling it visits rather than a speed it holds. Battery is the other constraint: running a model on the GPU drains a laptop quickly, so most serious local work happens plugged in.

Usable AI memory decides what a machine can load at all, and memory bandwidth decides how fast it answers once loaded. Those two numbers matter more than the processor name on the box. A machine with a large unified pool and modest bandwidth will run a very large model slowly; a machine with a fast narrow pool will run a small model quickly and refuse a large one outright.

Discrete or unified

Laptops split into two camps. Machines with a discrete NVIDIA GPU give a model only the card's VRAM, which caps the model size sharply but runs what fits very fast. Machines built on unified memory, whether Apple silicon or Ryzen AI Max, hand a much larger share of system memory to the model at lower bandwidth, so they load far bigger models and generate more slowly.