Device profile · macOS

Best local LLMs for Apple M3 Ultra (256GB)

Apple M3 Ultra (256GB) has ~192 GB estimated usable for weights, cache and runtime; 145 of 161 tracked models fit at the estimated 4K baseline. Best tool: LM Studio.

144 fit with room 1 tight fit 16 too heavy

Usable memory: ~192 GB
Estimated fits: 145
Too large: 16
Suggested fit: 30.5B

Suggested fit Q4_K_M

Qwen3 30B-A3B Yes

Runs at Q4_K_M using ~20.7 GB of ~192 GB usable. You have room for FP16 for higher quality.

The suggested model uses recorded Arena scores where available, then estimated fit and speed. Score coverage is partial; it does not establish the best model for every task. Fit is an estimate, not a benchmark on this device.

Set up Qwen3 30B-A3B on this device →

Estimated memory fits on Apple M3 Ultra (256GB)

Compatible models 145 total

Too large for this device

Kimi K3 DeepSeek-V4-Pro Kimi K2 Instruct Kimi K2.6 Kimi K2.7 Code GLM-5.2 DeepSeek R1 DeepSeek V3 DeepSeek-R1-0528 Nemotron 3 Ultra 550B-A55B Qwen3-Coder 480B-A35B Instruct MiniMax-M1-80k MiniMax M3 Llama 4 Maverick GLM-4.6 GLM-5.3-Flash

Best way to run models on macOS

Runtime guide macOS

Beginner: LM Studio, Polished GUI, ships MLX on Apple Silicon, one-click model downloads.

Power user: mlx-lm, Apple's MLX framework, usually the fastest on Apple Silicon for the same quant.

vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.

Full macOS tool guide →

FAQ

What is the best local LLM for Apple M3 Ultra (256GB)?

Qwen3 30B-A3B is a suggested fit, using ~20.7 GB at Q4_K_M of the ~192 GB usable on Apple M3 Ultra (256GB).

How much of Apple M3 Ultra (256GB)'s memory can I use for a model?

About 192 GB. Apple Silicon shares one unified memory pool; roughly 66-75% is available to the GPU for model weights, the rest is reserved for macOS.

Which tool should I use on macOS?

LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.) or mlx-lm for speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.