Best local AI models for a MacBook Pro M4 Pro with 24 GB
A MacBook Pro M4 Pro with 24 GB of unified memory has about 16.8 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.
24 GB unified memory · about 16.8 GB usable for a model · speeds estimated for M4 Pro, 20-core GPU
Buy a MacBook Pro M4 Pro on Amazon →
As an Amazon Associate we earn from qualifying purchases.
Last updated 4 November 2026 · refreshed after every Hugging Face sync · 15 models
| # | Model | Size | Memory | License | Downloads (30 days) | Likes |
|---|---|---|---|---|---|---|
| 1 | Qwen3-4B Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 8.4M | 783 |
| 2 | Qwen3.5-4B Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 6.4M | 1.2K |
| 3 | Ornith-1.5-9B-GGUF ornith-ai | 9B · GGUF | ~6 GB | MIT | 5.5M | 629 |
| 4 | Qwen3-VL-4B-Instruct Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 3.9M | 523 |
| 5 | Qwen3-4B-Instruct-2507 Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 2.7M | 1K |
| 6 | Qwen2.5-3B-Instruct Qwen | 3B · FP16 | ~8 GB | Custom (other) | 3M | 636 |
| 7 | Ornith-1.0-9B-GGUF ornith-ai | 9B · GGUF | ~6 GB | MIT | 2.4M | 699 |
| 8 | Llama-3.2-3B-Instruct meta-llama | 3B · FP16 | ~8 GB | Llama 3.2 license | 969.6K | 2.9K |
| 9 | NVIDIA-Nemotron-3-Nano-4B-BF16 nvidia | 4B · FP16 | ~10 GB | Custom (other) | 1.9M | 142 |
| 10 | LocateAnything-3B nvidia | 3B · FP16 | ~8 GB | Custom (other) | 322.5K | 3.2K |
| 11 | Qwen3-4B-Instruct-2507-FP8 Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 1.3M | 91 |
| 12 | Qwythos-9B-Claude-Mythos-5-1M-GGUF empero-ai | 9B · GGUF | ~6 GB | Apache 2.0 | 234.9K | 2.9K |
| 13 | gemma-4-12B-coder-fable5-composer2.5-v1-GGUF yuxinlu1 | 12B · GGUF | ~8 GB | Apache 2.0 | 110.7K | 3K |
| 14 | LFM2.5-24B-A2B-GGUF LiquidAI | 24B · GGUF | ~15 GB | Custom (other) | 148.6K | 235 |
| 15 | Ternary-Bonsai-2-9B-gguf prism-ml | 9B · GGUF | ~6 GB | Apache 2.0 | 111.6K | 124 |
How we rank
Language and vision-language models in GGUF, MLX, FP16 format whose published weights need about 5–16.8 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.