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AppleM2Mini PC

Best AI Models for Mac Mini M2 (16 GB)

Memory:16 GB UnifiedBandwidth:100.0 GB/sGPU Cores:10 GPU coresCPU Cores:8 CPU coresNeural Engine:15.8 TOPS

16 GB unified − 3.5 GB OS overhead = 12.5 GB available for AI models

How does this device compare with your machine?

37 of 257 featured models run great on Mac Mini M2 (16 GB) · best pick: Qwen3.5 9B at ~11 tok/s

largest that fits: 20.9B (GPT OSS 20B, Q4_K_S) · 32B needs Q2 or offload

What LLMs Can Mac Mini M2 (16 GB) Run?

918 models · 229 run great · 191 run well

Featured only

Score: model quality, 100 = average model. Fit: how well it runs on this hardware, S best to F won't fit. Ordered by quality, recency and Fit. How ranking works

Showing compatibility for Mac Mini M2 (16 GB)

LLM models compatible with Mac Mini M2 (16 GB) — ranked by quality, recency and Fit
ModelVRAMScoreFit
DeepSeek Coder v2 Lite Instruct
15.7B·Q4_K_M·33.3 t/s·164K ctx
10.2 GBA70, Runs well
Opt 125M
125M·FP16·250.0 t/s·2K ctx
0.3 GBS100, Runs great
Ornith 1.0 9B
9.4B·Q4_K_M·11.3 t/s·262K ctx
6.5 GBB50, Decent
Medgemma 1.5 4B IT
4.3B·Q4_K_M·24.6 t/s
5 GBA65, Runs well
LightOnOCR 3 4B
4.5B·Q4_K_M·21.9 t/s·262K ctx
3.4 GBB62, Decent
Tiny NemotronHForCausalLM Super
4M·Q4_K_M·7000.0 t/s·262K ctx
0.3 GBS100, Runs great
VulnLLM R 7B
7.6B·Q4_K_M·14.0 t/s·33K ctx
5.8 GBB53, Decent
Tiny NemotronHForCausalLM Ultra
4M·Q4_K_M·7000.0 t/s·262K ctx
0.3 GBS100, Runs great
Tiny Random Phi3
3M·Q4_K_M·233.3 t/s·4K ctx
0.3 GBS100, Runs great
Schematron 3B
3.2B·Q4_K_M·28.5 t/s·131K ctx
4.1 GBA69, Runs well
Gemma 3 270M IT
268M·Q4_K_M·388.9 t/s
1.2 GBS100, Runs great
EXAONE 3.5 2.4B Instruct
2.4B·Q4_K_M·44.0 t/s·33K ctx
3.4 GBA77, Runs well
Llama 3.2 11B Vision Instruct
10.7B·Q4_K_M·9.9 t/s
9.8 GBB48, Decent
Qwen2.5 1.5B Instruct
1.5B·Q4_K_M·54.7 t/s·33K ctx
1.7 GBA81, Runs well
MobileLLM R1.5 950M
950M·BF16·33.5 t/s
3.5 GBA72, Runs well
Harness 1
20.9B·Q3_K_M·28.5 t/s·131K ctx
10.8 GBB62, Decent

Mac Mini M2 (16 GB) has 12.5 GB of memory for models. In the ranking below, Qwen3.5 9B leads at Q4_K_M: it needs 6.6 GB (53% of that memory) and generates about 11 tokens per second. 37 of the 257 featured models earn a Fit S here, meaning they load with room for context and run fast. Decode speed is set mostly by memory bandwidth (100 GB/s), so smaller and mixture-of-experts models are noticeably quicker than dense models of the same quality. 32B-class models need about 22 GB at Q4_K_M, more than the 12.5 GB available; they fit only at Q2 with a quality loss, or by offloading layers to system RAM.

Runs 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)
  • Qwen3.5 2B (2.3B at Q4_K_M, ~39.5 tok/s)
  • ZAYA1 VL 8B (9.7B at Q4_K_M, ~69.1 tok/s)

Challenging

  • Qwen3.5 9B (9.7B at Q4_K_M, ~11 tok/s) fits but uses 53% of memory, so keep context short
  • Qwen3.8 27B (27.8B at IQ3_XXS, ~6.1 tok/s) fits but uses 96% of memory, so keep context short
  • Gemma 4 12B IT (12.0B at Q4_K_M, ~8.5 tok/s) fits but uses 72% of memory, so keep context short
  • 32B-class models need ~22 GB at Q4_K_M, so only Q2 fits
  • 70B-class models need ~46 GB at Q4_K_M, so they need CPU offload or more memory

Mac Mini M2 (16 GB) Specifications

Brand
Apple
Chip
M2
Type
Mini PC
Unified Memory
16 GB
Memory Bandwidth
100.0 GB/s
GPU Cores
10
CPU Cores
8
Neural Engine
15.8 TOPS
Release Date
2023-01-24

Get Started

Ollama (Recommended)

Runs Qwen3.5 9B on Q4_K_M; this is our top choice here.

$curl -fsSL https://ollama.com/install.sh | sh
$ollama run hf.co/unsloth/Qwen3.5-9B-GGUF:Q4_K_M

LM Studio

LM Studio

Download LM Studio, search for "Qwen3.5 9B", and run it with one click.

Prompt Processing

Estimated for Qwen3.5 9B, the compute-bound phase that reads your prompt before the first reply token appears.

74.2tok/s prefill

Short chat

6.9 s

512 tok prompt

Long chat

55.2 s

4,096 tok prompt

Document / codebase

442 s

32,768 tok prompt

Prefill is compute-bound and a different number from the decode tok/s shown elsewhere on this page — how prompt processing works →

Performance figures are estimates calibrated as of 2026-09-21 — see calibration basis →

What to do next with Mac Mini M2 (16 GB)

Which model to run first, a faster option with the same memory, and what more memory unlocks. Computed from the rankings and specs above.

Frequently Asked Questions

Can Mac Mini M2 (16 GB) run Qwen3.5 9B?

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.

How much memory is available for AI on Mac Mini M2 (16 GB)?

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.

Is Mac Mini M2 (16 GB) good for AI?

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.

What's the best model for Mac Mini M2 (16 GB)?

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.

How fast is Mac Mini M2 (16 GB) for AI inference?

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.

= (100 GB/s ÷ model GB) × efficiency

Apple Silicon achieves ~70% bandwidth efficiency thanks to and Metal acceleration.

Estimated speed on Mac Mini M2 (16 GB)

~11 tok/s
~6 tok/s
~9 tok/s
~22 tok/s

Real-world results typically within ±20%.

Learn more about tok/s estimation →

Can I run AI offline on Mac Mini M2 (16 GB)?

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.