If you want to run big AI models at home in late 2026, you've got three main paths: a Mac Studio, an RTX Spark laptop, or a desktop with a big graphics card. The difference comes down to one thing: where the memory is.
VRAM vs unified memory
- VRAM: memory on the graphics card itself. Very fast, but only the GPU uses it, and there isn't much. The model has to fit there to run at full speed.
- Unified memory: one pool shared by CPU and GPU. The GPU can use most of it for model weights. More room, usually less speed.
The three options, from official specs
| Mac Studio (M5 Ultra) | RTX Spark laptop | Desktop RTX 5090 | |
|---|---|---|---|
| Memory type | unified | unified LPDDR5X | 32GB GDDR7 VRAM |
| Max memory for models | up to 512GB | up to 128GB | 32GB |
| Memory bandwidth | 1.2TB/s | not headlined by NVIDIA | 1,792GB/s |
| Starting price | $5,499 (Ultra), $2,499 (M5 Max), U.S. | from $2,599 | $1,999 launch MSRP |
| Software | macOS, MLX | Windows, CUDA | Windows/Linux, CUDA |
Sources: Apple newsroom and specs; NVIDIA's RTX Spark announcement and product page; NVIDIA's RTX 5090 page.
One big caveat on the 5090: Tom's Hardware found it selling for about $8,500 on Amazon on Oct 17, 2026. More in why the RTX 5090 costs so much.
Capacity decides what fits
At 4-bit, a model needs roughly half a gigabyte per billion parameters, plus context. So:
- 32GB (RTX 5090): comfortably mid-size models. Bigger ones need offloading to RAM.
- 128GB (RTX Spark): NVIDIA says models up to 120B parameters with 1M context.
- 512GB (M5 Ultra): the very large open-weight models Apple calls "frontier-class."
Bandwidth decides speed
Each new token reads through the model weights, so bandwidth roughly caps how fast text comes out. The 5090 is fastest for models that fit in 32GB. The Mac Studio is slower per byte but holds far more. RTX Spark sits in the middle on capacity, and NVIDIA doesn't headline its bandwidth, so wait for independent tests.
How to choose
- Your models fit in ~30GB, you want speed: desktop GPU (if you can find one at a sane price).
- You want CUDA, Windows and portability with big models: RTX Spark.
- You want the biggest models on one desk: Mac Studio M5 Ultra.
- You only need big models sometimes: rent a GPU.
Size your setup with the local AI hardware sizing sheet.
