Skip to content
localmodel.run

Device profile · macOS

Best local LLMs for Apple M1 (8GB)

Apple M1 (8GB) has ~5.5 GB usable for model weights and runs 24 of 67 popular models. Best tool: LM Studio.

Usable memory
~5.5 GB
Models run
24
Too large
43
Top pick
4B
Top pick Q4_K_M
Gemma 3 4B Yes

Runs at Q4_K_M using ~3.8 GB of ~5.5 GB usable. You have room for Q8_0 for higher quality.

Runs on Apple M1 (8GB)

Compatible models 24 total

Too large for this device

DeepSeek R1DeepSeek V3Qwen3 235B A22Bgpt-oss 120BLlama 4 ScoutSarvam-105BQwen2.5 72BLlama 3.3 70BMixtral 8x7BCommand R 35BYi 1.5 34BQwen2.5 32BQwen3 32BDeepSeek-R1-Distill-Qwen 32BQwen2.5 Coder 32BGranite 4.0 H SmallQwen3 30B-A3BSarvam-30BGemma 2 27BGemma 3 27BMistral Small 3 24BSarvam-M 24Bgpt-oss 20BDeepSeek-V2-LitePhi-4 14BQwen2.5 14BQwen3 14BDeepSeek-R1-Distill-Qwen 14BQwen2.5 Coder 14BMistral Nemo 12BGemma 3 12BLlama 3.2 Vision 11BFalcon3 10BGemma 2 9BGLM-4 9BQwen2.5-VL 7BLlama 3.1 8BQwen3 8BDeepSeek-R1-Distill-Llama 8BMistral 7BQwen2.5 7BDeepSeek-R1-Distill-Qwen 7BQwen2.5 Coder 7B

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 M1 (8GB)?

Gemma 3 4B is the strongest model that runs comfortably, using ~3.8 GB at Q4_K_M of the ~5.5 GB usable on Apple M1 (8GB).

How much of Apple M1 (8GB)'s memory can I use for a model?

About 5.5 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.

Sources

Memory figures are estimates. See methodology.