Best open models for a Mac with 32 GB

An Apple silicon Mac with 32 GB of unified memory can run language models and vision-language models using MLX or llama.cpp (via LM Studio or Ollama). With roughly 24 GB available for the model after the system and apps, you can run models needing 10–24 GB.

Last updated 3 November 2026 · refreshed after every Hugging Face sync · 15 models

#ModelSizeMemoryLicenseDownloads (30 days)Likes
1Ternary-Bonsai-2-27B-gguf
prism-ml
27B · GGUF~17 GBApache 2.01.8M2.9K
2Ornith-1.5-35B-A3B-GGUF
ornith-ai
35B · GGUF~21 GBMIT4M571
3Qwen3.8-27B-GSQ-RCO-GGUF
ISTA-DASLab
27B · GGUF~17 GBApache 2.01.3M2.5K
4Ornith-1.0-35B-GGUF
ornith-ai
35B · GGUF~21 GBMIT1.5M1.1K
5Qwen3.8-27B-MLX-4bit
lmstudio-community
27B · MLX~17 GBApache 2.03.6M116
6Qwen3-Coder-30B-A3B-Instruct-GGUF
unsloth
30B · GGUF~18 GBApache 2.01.1M1.2K
7Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF
ukisai
27B · GGUF~17 GBCustom (other)1.6M478
8Qwen3.8-35B-A3B-Distill-GGUF
empero-ai
35B · GGUF~21 GBApache 2.01.3M257
9Underdog-Saluki-27B-1.0
ConwayResearch
27B · GGUF~17 GBApache 2.0647.4K671
10Bonsai-27B-mlx-1bit
prism-ml
27B · MLX~17 GBApache 2.0976.6K287
11Ternary-Bonsai-27B-mlx-2bit
prism-ml
27B · MLX~17 GBApache 2.0986K220
12Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2
nerkyor
27B · GGUF~17 GBApache 2.0430.8K485
13Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP-GGUF
DavidAU
27B · GGUF~17 GBApache 2.0580.2K254
14Qwen2.5-32B-Instruct-GGUF
bartowski
32B · GGUF~20 GBApache 2.0843.5K93
15MiniCPM-V-4.7-35B-A3B-gguf
openbmb
35B · GGUF~21 GBApache 2.0198.2K285

How we rank

Language and vision-language models in MLX or GGUF format whose published weights need about 10–24 GB. We include models seen on Hugging Face in the last 14 days with at least 10,000 downloads in the last 30 days. Score = log10(30-day downloads) + ½ × log10(likes + 1), so popularity leads and likes break near-ties. We keep one entry per model name and leave out adult and uncensored builds. Memory is estimated from parameter count and the published file format (16-bit ≈ 2 bytes per parameter, 4-bit ≈ 0.5) plus 20% headroom; long contexts need more. A quantised copy of a bigger model may also fit — check the model page.

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