Guides · Hardware · 6 min read
Mac or Nvidia for local AI? The honest comparison
Two camps dominate local AI at home: people with an Nvidia card in a PC, and people with an Apple Silicon Mac. Each side has a real advantage, and which one matters depends on what you want to run.
Capacity: Mac wins
Apple Silicon uses unified memory shared by CPU and GPU. A Mac Studio with 128 GB can load a 70B model at Q8 or a 120B mixture-of-experts model, something no consumer Nvidia card can do. Even a 32 GB MacBook Pro comfortably runs 27B to 32B models that would need a 24 GB RTX 4090. If you want the biggest, smartest models, a Mac with a lot of memory is the cheapest path.
Speed: Nvidia wins
Generation speed is governed by memory bandwidth. An RTX 4090 reads memory at about 1,000 GB/s and a 5090 at nearly 1,800. A base M4 manages around 120 GB/s, an M4 Pro 273, an M4 Max 546. For an 8B model that is the difference between 100 tokens per second on a 4090 and 25 on an M4 Max. Nvidia also has the broadest software support: every new inference trick ships for CUDA first.
Price and practicality
A used RTX 3090 with 24 GB costs a fraction of a Mac Studio and runs 32B models fast, but you need a desktop with a big power supply. A Mac runs quietly on a desk and doubles as a normal computer. Laptops are a special case: Nvidia laptop cards top out at 16 GB and throttle under load, so for a portable local AI machine a MacBook Pro with 36 GB or more is hard to beat.
Rough guide
| You want | Pick |
|---|---|
| Fastest chat with 8B to 14B models | Any Nvidia card with 12 GB or more |
| Run 32B models well | RTX 3090 / 4090 / 5090, or Mac with 48 GB+ |
| Run 70B and larger | Mac Studio 96 GB+, or two 24 GB cards |
| A laptop that does it all | MacBook Pro M4 Max, 48 GB or more |
| Cheapest entry | Used RTX 3060 12 GB |
Put your actual machine into the checker to see the specific list for your memory and bandwidth.