# Mac or PC for local AI? A plain-words guide

URL: https://insidethemachine.ai/learn/mac-or-pc
Markdown: https://insidethemachine.ai/learn/mac-or-pc.md
Read: 4 minutes
Data: Prices and speeds: machines.json, checked 2026-10-15. Model sizes: models.json, 2026-10-26.

There is no one winner. Memory decides which AI models fit. Then speed decides how it feels.

## 1. Four kinds of computer can run AI.

The site sorts every computer into one of these four.

1. **Mac**: One box. Memory is shared by the chip and the graphics.
2. **PC with a graphics card**: The model runs in the card's own memory. Prices are for the card alone.
3. **Mini PC or AI box**: A small PC with lots of shared memory, built for AI.
4. **Small board**: Tiny, cheap and low power. Slow for chat.

"Shared memory" means one pool of memory for the main chip and the graphics. The AI can use most of it. A graphics card has its own memory, called VRAM (video memory). On a PC with a card, the model runs in the VRAM.

## 2. Rule 1: the model must fit in memory.

Too big, and it is very slow or does not start.

- On a Mac, we count about 75% of the memory as usable for AI. The rest is for the system and your apps. (This is the macOS default.)
- On a PC with a graphics card, the card's VRAM counts, not the computer's memory. We count all of it but 1 GB.
- A mini PC or AI box shares its memory like a Mac. We use the share its maker gives the graphics.
- A small board is cheap and uses little power, but it is slow for chat.

Example: the open model gpt-oss-120b needs about 74.2 GB of memory. Its little brother gpt-oss-20b needs about 15.4 GB. Here is which computer fits the big one.

Does gpt-oss-120b (needs about 74.2 GB) fit? One example per kind.

| Computer | Kind | Memory | Usable for AI | Fits gpt-oss-120b? | Price |
|---|---|---|---|---|---|
| Mac Studio (M5 Max) | Mac | 128 GB | 96 GB | Yes | $5,099 (worked out from list prices, macworld.com, 2026-10-15) |
| GeForce RTX 5090 | PC with a graphics card | 32 GB | 31 GB | No | $5,700 (lowest new price in stock, rtx.live, 2026-10-15), card only |
| Framework Desktop (Ryzen AI Max+ 395) | Mini PC or AI box | 128 GB | 96 GB | Yes | $3,449 (list price, computingforgeeks.com, 2026-10-15), DIY kit, out of stock |
| Raspberry Pi 5 (16GB), CPU only | Small board | 16 GB | 12 GB | No | $305 (list price, computingforgeeks.com, 2026-10-12), board only |

So the GeForce RTX 5090 is a very fast card, but its 32 GB is too small for the big model. The Mac Studio (M5 Max) with 128 GB holds it.

## 3. Rule 2: then speed decides how it feels.

Speed is counted in tokens per second (tok/s). A token is a piece of a word.

What sets speed is memory bandwidth: how fast the computer can read the model. A graphics card reads its VRAM very fast. Published tests:

- Mac Studio (M5 Max): **78.2 tok/s** on gpt-oss-120b (oMLX v0.3.9; test machine: M5 Max 40c, 128GB). Source: [omlx.ai](https://omlx.ai/benchmarks/dgadv0k9), 2026-10-15.
- GeForce RTX 5090: **318.4 tok/s** on gpt-oss-20b (llama.cpp; test machine: RTX 5090 + Ryzen 7 9850X3D (chat-short workload)). Source: [llm-speed.com](https://llm-speed.com/m/gpt-oss-20b), 2026-10-15.
- Framework Desktop (Ryzen AI Max+ 395): **54 tok/s** on gpt-oss-120b (llama.cpp Vulkan; test machine: Framework Desktop 128GB (llama-bench pp512/tg128)). Source: [llamaperf.com](https://llamaperf.com/model/gpt-oss), 2026-10-15.
- Raspberry Pi 5 (16GB), CPU only: **5.6 tok/s** on llama3.2 3B (Ollama; test machine: Pi 5 8GB CPU (Jeff Geerling)). Source: [computingforgeeks.com](https://computingforgeeks.com/edge-llm-jetson-orin-vs-ai-hat-2-vs-hailo-10h/), 2026-10-12.

Bandwidth in our data: Mac Studio (M5 Max) 614 GB/s, GeForce RTX 5090 1792 GB/s, Framework Desktop (Ryzen AI Max+ 395) 256 GB/s, Raspberry Pi 5 (16GB), CPU only 17 GB/s. Tests use different engines and settings, so compare them loosely. What a token is: [What is a token?](https://insidethemachine.ai/learn/tokens)

## 4. So which one?

Start from what you want to run, not from the brand.

1. **You have a computer already**: Use it first. [Scan my machine](https://insidethemachine.ai/my) reads your memory, chip and graphics card with one read-only command and lists what fits.
2. **You want big models**: Look for lots of memory: a Mac or a mini PC with shared memory. The [Machines](https://insidethemachine.ai/machines) page lists each one with its price and source.
3. **You want speed on small models**: A PC with a strong graphics card is very fast, as long as the model fits in its VRAM. Card prices are for the card alone: you also need a PC.
4. **Windows or Mac steps**: Our recipes are tested on a Mac first. The Windows steps are there, but we have not tested them yet.

## Next

- [Compare machines](https://insidethemachine.ai/machines) (markdown: https://insidethemachine.ai/machines.md)
- [Scan my machine](https://insidethemachine.ai/my) (markdown: https://insidethemachine.ai/my.md)
- [What is a token?](https://insidethemachine.ai/learn/tokens) (markdown: https://insidethemachine.ai/learn/tokens.md)

More guides: https://insidethemachine.ai/learn.md