Best AI Models for Mac Mini M2 (16 GB)

Yes. The Mac Mini M2 (16 GB) with 16 GB unified memory can run Qwen3.5 9B (Q4_K_M, 6.6 GB, 53% of memory). It is also the table's best pick, at ~11 tok/s. 37 of the 257 featured models get a Fit of S (runs great) here. The largest featured model that fits at Q4 or better is GPT OSS 20B (20.9B at Q4_K_S). 32B-class models need about 22 GB at Q4_K_M, so only Q2 (with a quality loss) or CPU offload gets them running in 12.5 GB. Check the compatibility table above for the full list with Fit, VRAM usage and estimated speed. Models are checked against the 12.5 GB available to AI (that is, the 16 GB of RAM minus roughly 3.5 GB for macOS), while Apple Silicon's unified memory allows the GPU to draw from that same pool without copying data.

The Mac Mini M2 (16 GB) has 16 GB unified memory. After macOS reserves ~3.5 GB for the operating system, approximately 12.5 GB is available for AI models. Model rankings here use that 12.5 GB limit instead of the total 16 GB. Unlike discrete GPUs where VRAM is separate from system RAM, a unified-memory device shares one memory pool between the CPU and GPU — this means no data copying overhead, but you share memory with macOS and open apps.

With 16 GB unified memory and 100.0 GB/s bandwidth, the Mac Mini M2 (16 GB) is good for running local AI models. Models that run well include Ling 3.0 Tiny (7.9B at Q4_K_M, ~55.1 tok/s); LFM2.5 8B A1B (8.5B at Q4_K_M, ~49.1 tok/s); MiniCPM5 2B (2.5B at Q4_K_M, ~36.8 tok/s). It's a capable entry point for small models, and unified memory makes it efficient despite the modest size.

For the Mac Mini M2 (16 GB), the best pick right now is Qwen3.5 9B at ~11 tok/s. Also running well: Ling 3.0 Tiny (7.9B at Q4_K_M, ~55.1 tok/s); LFM2.5 8B A1B (8.5B at Q4_K_M, ~49.1 tok/s); MiniCPM5 2B (2.5B at Q4_K_M, ~36.8 tok/s). The best choice depends on your use case: coding assistants benefit from code-tuned models, general chat from instruction-tuned ones. The list is ranked by quality, recency and Fit.

With 100.0 GB/s memory bandwidth, the Mac Mini M2 (16 GB) achieves approximately 16 tok/s on a 7B model at Q4_K_M — that's functional for interactive use. A 14B model runs at ~8 tok/s. In the ranking, the top pick Qwen3.5 9B runs at ~11 tok/s. Apple Silicon achieves high efficiency (~70%) thanks to unified memory — there's no PCIe bottleneck between CPU and GPU.

Yes — once you download a model, it runs entirely on the Mac Mini M2 (16 GB) without internet. Applications like Ollama and LM Studio make it straightforward to download, manage, and run models locally. All your conversations stay private on your device with zero data sent to external servers. This is one of the key advantages of local AI: complete privacy, no API costs, and no rate limits.