Best Local LLM for a Mac M4 with 16GB
On a 16 GB M4 Mac the sweet spot is the 7B to 8B class: Qwen3 8B and Llama 3.1 8B both need about 5 GB at Q4, which leaves plenty of unified memory for macOS. Ollama and mlx-lm run natively with Metal acceleration, so there is no driver setup at all.
Model data last verified on June 27, 2026. All VRAM figures are for the Q4 quantization Ollama serves by default.
The hardware in question
Apple Mac mini M4 (16 GB)
$799
M4 chip, 10-core CPU, 10-core GPU, 16 GB unified memory, 120 GB/s bandwidth
Best zero-friction entry point for Mac-native local AI
Full entry in the hardware guideEvery model that fits 16 GB of unified memory
12 of the 16 models in our verified catalog fit this budget at Q4, sorted with the largest fit first. Click any model for its full spec sheet, hardware cross-reference and run commands.
| Model | Params | Quant | Min VRAM | Context | Fit |
|---|---|---|---|---|---|
| Gemma 3 27B | 27B | Q4 | 15 GB | 128K | Tight fit |
| OpenAI gpt-oss-20bHistoric first | 20B total / 3.6B active (MoE) | Q4 | 12 GB | 128K | Tight fit |
| Gemma 4 12BBest for fine-tuning | 12B | Q4 | 8 GB | 256K | 8 GB free |
| Phi-4 14BBest small coder | 14B | Q4 | 8 GB | 16K | 8 GB free |
| Mistral Nemo 12B | 12B | Q4 | 7 GB | 128K | 9 GB free |
| Llama 3.1 8B | 8B | Q4 | 5 GB | 128K | 11 GB free |
| Qwen3 8BMost popular | 8B | Q4 | 5 GB | 128K | 11 GB free |
| DeepSeek R1 Distill 8BMost private | 8B | Q4 | 5 GB | 128K | 11 GB free |
| Mistral 7B v0.3 | 7B | Q4 | 4 GB | 32K | 12 GB free |
| Qwen 2.5 Coder 7B | 7B | Q4 | 4 GB | 128K | 12 GB free |
| Gemma 3 4B | 4B | Q4 | 3 GB | 128K | 13 GB free |
| Llama 3.2 3BFastest | 3B | Q4 | 2 GB | 128K | 14 GB free |
Unified memory is shared with macOS, so models within 4 GB of the ceiling are flagged as tight fits. Expect to close other apps or drop to a smaller quantization for those.
Run the top picks with Ollama
One command each. Ollama pulls the Q4_K_M build by default and exposes an OpenAI-compatible endpoint at localhost:11434/v1.
ollama run gemma3:27bollama run gpt-oss:20bollama run gemma4:12bFrequently asked questions
What is the best local LLM for a 16GB M4 Mac?
Qwen3 8B or Llama 3.1 8B. Both need about 5 GB at Q4, which leaves most of the 16 GB unified pool free for macOS and your other apps. Larger models like Gemma 3 27B technically fit the number but leave almost no headroom, so we flag them as tight fits.
Can a 16GB Mac run a 13B model?
Yes, at Q4 or Q5 quantization, not at full precision. Mistral Nemo 12B needs about 7 GB at Q4 and runs comfortably. Because unified memory is shared with macOS, keep a few gigabytes free rather than loading right up to the ceiling.
Do I need to configure a GPU on a Mac?
No. Ollama and mlx-lm use Apple's Metal acceleration out of the box, with no driver or CUDA setup. The M4 Mac mini draws only 10 to 30 W during inference and stays completely silent under typical load.
Is 16GB of unified memory enough for local AI?
Yes for the 7B to 12B class, which covers most everyday chat and coding use. But 16 GB is a hard ceiling with no upgrade path, since the memory is part of the chip package. If you want the 27B class, buy the 24 GB or 48 GB configuration up front.
Different budget or use case?
The faceted model browser combines VRAM, family, license and task filters over the same verified catalog.