Device profile · macOS

Best local LLMs for Apple M4 (16GB)

Apple M4 (16GB) has ~10.5 GB estimated usable for weights, cache and runtime; 76 of 161 tracked models fit at the estimated 4K baseline. Best tool: LM Studio.

74 fit with room 2 tight fit 85 too heavy

Usable memory: ~10.5 GB
Estimated fits: 76
Too large: 85
Suggested fit: 12B

Suggested fit Q4_K_M

Gemma 3 12B Yes

Runs at Q4_K_M using ~8.9 GB of ~10.5 GB usable.

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 Gemma 3 12B on this device →

Estimated memory fits on Apple M4 (16GB)

Compatible models 76 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 DeepSeek-V4-Flash Qwen3 235B A22B dots.llm1 Devstral 2 123B Nemotron 3 Super 120B-A12B Nemotron 3.5 Super VL 120B-A12B Laguna S 2.1 gpt-oss 120B Command A Llama 4 Scout GLM-4.5-Air Sarvam-105B Hunyuan-A13B-Instruct Qwen3-Coder-Next 80B-A3B Qwen2.5 72B Llama 3.3 70B Llama-3.3-Nemotron-Super-49B-v1 Mixtral 8x7B Flint 40B-A2.5B Seed-OSS 36B Instruct Qwen3.6 35B-A3B Command R 35B Ornith 1.0 35B Ornith 1.5 35B-A3B Qwen-AgentWorld 35B-A3B Yi 1.5 34B Falcon-H1-34B-Instruct Laguna XS 2.1 Gemma 4 31B Qwen2.5 32B Qwen3 32B DeepSeek-R1-Distill-Qwen 32B Qwen2.5 Coder 32B Granite 4.0 H Small GLM-4-32B-0414 EXAONE 4.0 32B OLMo 2 32B Instruct Olmo 3.1 32B Instruct Qwen3-VL 30B-A3B Qwen3 30B-A3B Qwen3-Coder 30B-A3B North Mini Code 1.0 Sarvam-30B Nemotron 3 Nano 30B-A3B Nemotron Cascade 2 30B-A3B GLM-4.7-Flash Muse Glimmer 30B Nemotron 3.5 Lightning 30B-A3B Granite 4.2 30B Granite 4.1 30B Qwen3.6 27B Gemma 2 27B Gemma 3 27B Qwen3.8 27B Gemma 4 26B-A4B Mistral Small 3 24B Sarvam-M 24B Mistral Small 3.1 24B Magistral Small Devstral Small LFM2 24B-A2B Devstral Small 2 24B gpt-oss 20B ERNIE 4.5 21B-A3B DeepSeek-V2-Lite Phi-4 14B Qwen2.5 14B Qwen3 14B DeepSeek-R1-Distill-Qwen 14B

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 M4 (16GB)?

Gemma 3 12B is a suggested fit, using ~8.9 GB at Q4_K_M of the ~10.5 GB usable on Apple M4 (16GB).

How much of Apple M4 (16GB)'s memory can I use for a model?

About 10.5 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.