MacBook Pro, PC duel for local LLMs

Hot take: if the goal is to learn local inference broadly, a PC with an NVIDIA GPU is usually the better lab bench; if the goal is portability plus the ability to fit bigger models on one machine, the Mac starts to make sense.

• –The Mac argument is unified memory: you can load larger models and keep the whole setup portable, which matters if you want one machine for work and experimentation.
• –The PC argument is CUDA, upgradeability, and price/performance; for agentic workflows and heavier tinkering, desktop GPUs still dominate.
• –The thread makes a useful distinction many buyers miss: "learning LLMs" does not require 128GB on day one, but "running bigger local models comfortably" often does.
• –If you do not already know your target models, context sizes, and throughput needs, the safest move is usually to start cheaper and scale up once the bottleneck is obvious.
• –For a backend/full-stack developer, the fastest path is often a modest setup first, then a bigger hardware buy after you know whether your bottleneck is memory, compute, or just model quality.