Best local AI models for a MacBook Pro M4 Max with 48 GB
A MacBook Pro M4 Max with 48 GB of unified memory has about 33.6 GB free for a model once macOS and your apps are running. These are the most downloaded open language and vision-language models that fit, for LM Studio, Ollama, llama.cpp or MLX.
48 GB unified memory · about 33.6 GB usable for a model · speeds estimated for M4 Max, 40-core GPU
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Last updated 4 November 2026 · refreshed after every Hugging Face sync · 15 models
| # | Model | Size | Memory | License | Downloads (30 days) | Likes |
|---|---|---|---|---|---|---|
| 1 | Llama-3.1-8B-Instruct meta-llama | 8B · FP16 | ~20 GB | Llama 3.1 license | 4.5M | 8.8K |
| 2 | Qwen3-8B Qwen | 8B · FP16 | ~20 GB | Apache 2.0 | 7.5M | 2.2K |
| 3 | Qwen3.5-9B Qwen | 9B · FP16 | ~22 GB | Apache 2.0 | 6.4M | 2.3K |
| 4 | Qwen2.5-7B-Instruct Qwen | 7B · FP16 | ~17 GB | Apache 2.0 | 6.1M | 2.5K |
| 5 | Qwen2.5-VL-7B-Instruct Qwen | 7B · FP16 | ~17 GB | Apache 2.0 | 5.9M | 1.8K |
| 6 | Qwen3-VL-8B-Instruct Qwen | 8B · FP16 | ~20 GB | Apache 2.0 | 6.9M | 1.2K |
| 7 | Ornith-1.5-35B-A3B-GGUF ornith-ai | 35B · GGUF | ~21 GB | MIT | 4.1M | 576 |
| 8 | Mistral-7B-Instruct-v0.2 mistralai | 7B · FP16 | ~17 GB | Apache 2.0 | 1.7M | 3.3K |
| 9 | Ternary-Bonsai-2-27B-gguf prism-ml | 27B · GGUF | ~17 GB | Apache 2.0 | 1.8M | 2.9K |
| 10 | Qwen3.8-27B-GSQ-RCO-GGUF ISTA-DASLab | 27B · GGUF | ~17 GB | Apache 2.0 | 1.3M | 2.5K |
| 11 | Ornith-1.0-35B-GGUF ornith-ai | 35B · GGUF | ~21 GB | MIT | 1.5M | 1.1K |
| 12 | Qwen2.5-Coder-7B-Instruct Qwen | 7B · FP16 | ~17 GB | Apache 2.0 | 1.3M | 859 |
| 13 | Qwen3.8-27B-MLX-4bit lmstudio-community | 27B · MLX | ~17 GB | Apache 2.0 | 3.6M | 116 |
| 14 | Qwen3-Coder-30B-A3B-Instruct-GGUF unsloth | 30B · GGUF | ~18 GB | Apache 2.0 | 1.1M | 1.2K |
| 15 | Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF ukisai | 27B · GGUF | ~17 GB | Custom (other) | 1.6M | 482 |
How we rank
Language and vision-language models in GGUF, MLX, FP16 format whose published weights need about 10–33.6 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.