Best local AI models for a Mac mini with 16 GB
A Mac mini with 16 GB of unified memory has about 11.2 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.
16 GB unified memory · about 11.2 GB usable for a model · speeds estimated for M4
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Last updated 3 November 2026 · refreshed after every Hugging Face sync · 15 models
| # | Model | Size | Memory | License | Downloads (30 days) | Likes | Est. speed (Q4) |
|---|---|---|---|---|---|---|---|
| 1 | Qwen3-4B Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 8.4M | 782 | – |
| 2 | Qwen3.5-4B Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 6.4M | 1.2K | ~35 tok/s |
| 3 | Qwen2.5-1.5B-Instruct Qwen | 1.5B · FP16 | ~4 GB | Apache 2.0 | 4.6M | 928 | ~82 tok/s |
| 4 | Ornith-1.5-9B-GGUF ornith-ai | 9B · GGUF | ~6 GB | MIT | 5.5M | 626 | – |
| 5 | Qwen3-1.7B Qwen | 1.7B · FP16 | ~5 GB | Apache 2.0 | 4.3M | 612 | – |
| 6 | Qwen3-VL-4B-Instruct Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 3.9M | 523 | – |
| 7 | Qwen3-4B-Instruct-2507 Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 2.7M | 1K | – |
| 8 | Qwen2.5-3B-Instruct Qwen | 3B · FP16 | ~8 GB | Custom (other) | 3M | 633 | ~45 tok/s |
| 9 | Ornith-1.0-9B-GGUF ornith-ai | 9B · GGUF | ~6 GB | MIT | 2.4M | 699 | – |
| 10 | Llama-3.2-3B-Instruct meta-llama | 3B · FP16 | ~8 GB | Llama 3.2 license | 969.6K | 2.9K | ~38 tok/s |
| 11 | MiniCPM5-2B openbmb | 2B · FP16 | ~5 GB | Apache 2.0 | 1.1M | 1.8K | – |
| 12 | DeepSeek-R1-Distill-Qwen-1.5B deepseek-ai | 1.5B · FP16 | ~4 GB | MIT | 784.6K | 1.7K | – |
| 13 | NVIDIA-Nemotron-3-Nano-4B-BF16 nvidia | 4B · FP16 | ~10 GB | Custom (other) | 1.9M | 142 | – |
| 14 | LocateAnything-3B nvidia | 3B · FP16 | ~8 GB | Custom (other) | 322.5K | 3.2K | – |
| 15 | Qwen3-4B-Instruct-2507-FP8 Qwen | 4B · FP16 | ~10 GB | Apache 2.0 | 1.3M | 91 | – |
How we rank
Language and vision-language models in GGUF, MLX, FP16 format whose published weights need about 3–11.2 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.
Speed data: ModelFit (CC BY 4.0), from its open dataset, refreshed weekly. These are modelled estimates of decode speed for a 4-bit (Q4) build of the same model at a 4K context, not measured benchmarks, and they are not an endorsement by ModelFit. A dash means ModelFit has no estimate for that model.