Best Mac for local LLMs in 2026
A Mac is the easiest way to run a very large model at home, and the hardest way to run a small one fast. Unified memory means the whole machine's RAM is available to the model, so a 256 GB Mac Studio holds a 235B mixture-of-experts model that no single GPU can, and it does it silently from a wall socket. The trade is bandwidth: 819 GB/s is excellent for a computer and modest next to a GPU's memory, and prompt processing on Apple silicon is slow enough that long documents feel it.
Prices are the cheapest live eBay listing that passes our filters, item plus shipping, refreshed every 15 minutes. Speed is our estimate for Qwen3.8 27B · Q4_K_M with 8K of context.
The picks
Buy memory, not cores. The CPU and GPU core counts barely move token speed; the memory size decides what you can run at all, and the memory bandwidth, which goes up with the bigger chips, decides how fast.
Apple Mac Studio M3 Ultra, 512 GB
$17,499$34/GB
512 GB · 819 GB/s · ~30 tok/s
The only desktop that holds a 400B-class model: 512 GB of unified memory at 819 GB/s. Decode is quick for the size; prefill is the weak point, since there are no tensor cores and the Neural Engine sits unused by llama.cpp.
Apple Mac Studio M3 Ultra, 256 GB
$8,049.99$31/GB
256 GB · 819 GB/s · ~30 tok/s
256 GB at 819 GB/s: a 235B mixture-of-experts model at Q4, or a 70B at FP16, in a silent box. Half the price of the 512 GB configuration.
Apple Mac Studio M4 Max, 128 GB
$4,899$38/GB
128 GB · 546 GB/s · ~20 tok/s
The same 128 GB as a DGX Spark with twice the bandwidth, in a quiet desktop that is also a Mac. A 70B model at Q4 runs at a usable pace; no CUDA.
Where Macs fall short
Three things. Prompt processing: feeding a long document in takes several times longer than on an NVIDIA card with the same model. Software: most things run through llama.cpp or MLX, both excellent, but anything CUDA-only is out. Resale: Apple memory upgrades cost far more than the equivalent in GPUs, and you cannot add a second one later.
The alternatives at 128 GB
At 128 GB a Mac competes with the GB10 boxes and the Strix Halo desktops. The Mac has more bandwidth than either; the others run Linux and CUDA or ROCm. The DGX Spark guide lines them up.
NVIDIA DGX Spark
$7,600$59/GB
128 GB · 273 GB/s · ~11 tok/s
128 GB of unified memory with the full CUDA stack in a box the size of a paperback. Bandwidth is the limit: 273 GB/s means a 70B model at Q4 fits but talks at a few tokens per second. Two units link over 200 GbE for 256 GB.
Framework Desktop, Ryzen AI Max+ 395, 128 GB
$3,464.65$27/GB
128 GB · 256 GB/s · ~7.8 tok/s
128 GB of unified memory on x86 for half the price of a DGX Spark. About 96 GB can be handed to the GPU on Linux, enough for a 70B model at Q6. Bandwidth matches the Spark's, so speed does too.
More guides
- Best GPUs for local AI in 2026, by budget Every budget tier from $300 up, picked by memory first, priced live.
- DGX Spark alternatives in 2026: the same chip, half the price, or more bandwidth GB10 twins, Strix Halo boxes, Apple and used cards, lined up.
- Used server GPUs for local LLMs in 2026: P40, P100, MI50, A-series and A100 The cheap-memory cards and their fine print: cooling, power, drivers.