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AppleM2Mini PC

Best AI Models for Mac Mini M2 (8 GB)

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

8 GB unified − 3.5 GB OS overhead = 4.5 GB available for AI models

How does this device compare with your machine?

36 of 143 featured models run great on Mac Mini M2 (8 GB) · best pick: Qwen3.5 4B at ~21.5 tok/s

largest that fits: 7B (Baichuan2 7B Chat, IQ4_XS) · 14B needs CPU offload or more memory

What LLMs Can Mac Mini M2 (8 GB) Run?

143 models · 36 run great · 37 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 (8 GB)

LLM models compatible with Mac Mini M2 (8 GB) — ranked by quality, recency and Fit
ModelVRAMScoreFit
Qwen3.5 4B
4.7B·Q4_K_M·21.5 t/s·262K ctx
3.4 GBB62, Decent
Gemma 4 E2B IT
5.1B·Q4_K_M·20.4 t/s·131K ctx
3.5 GBB60, Decent
MiniCPM5 2B
2.5B·Q4_K_M·36.8 t/s·131K ctx
2.5 GBA74, Runs well
Qwen3.5 2B
2.3B·Q4_K_M·39.5 t/s·262K ctx
1.9 GBA75, Runs well
LFM2.5 2.6B
2.7B·Q4_K_M·34.3 t/s·131K ctx
2.3 GBA73, Runs well
MinerU2.5 Pro 2605 1.2B
1.2B·Q4_K_M·68.6 t/s·8K ctx
1.1 GBS85, Runs great
MiniCPM V 4.6
1.3B·Q4_K_M·61.9 t/s·262K ctx
1.5 GBA84, Runs well
D1 Omni 600M
587M·Q4_K_M·97.2 t/s·128K ctx
1.2 GBS91, Runs great
MiniCPM V 4.6 Thinking
1.3B·Q4_K_M·61.9 t/s·262K ctx
1.5 GBA84, Runs well
LFM2.5 VL 450M
449M·Q4_K_M·109.4 t/s·128K ctx
0.8 GBS93, Runs great
Cosmos Reason2 2B
2.4B·Q4_K_M·43.5 t/s
3.5 GBA77, Runs well
LocateAnything 3B
3.8B·Q4_K_M·26.2 t/s·33K ctx
3.2 GBA67, Runs well
MinerU2.5 Pro 2604 1.2B
1.2B·Q4_K_M·68.6 t/s·8K ctx
1.1 GBS85, Runs great
D1 3B
3.1B·Q4_K_M·30.4 t/s·33K ctx
2.6 GBA71, Runs well
FrogNano 4B 2609
4.7B·Q4_K_M·21.5 t/s·262K ctx
3.4 GBB62, Decent
Unlimited OCR
3.3B·Q4_K_M·70.1 t/s·33K ctx
3.3 GBS86, Runs great

Mac Mini M2 (8 GB) has 4.5 GB of memory for models. In the ranking below, Qwen3.5 4B leads at Q4_K_M: it needs 3.4 GB (76% of that memory) and generates about 21.5 tokens per second. 36 of the 143 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. 14B-class models need about 10 GB at Q4_K_M, more than the 4.5 GB available, so they require offloading layers to system RAM or a larger-memory setup.

Runs Well

  • 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)
  • LFM2.5 2.6B (2.7B at Q4_K_M, ~34.3 tok/s)
  • MinerU2.5 Pro 2605 1.2B (1.2B at Q4_K_M, ~68.6 tok/s)
  • MiniCPM V 4.6 (1.3B at Q4_K_M, ~61.9 tok/s)

Challenging

  • Qwen3.5 4B (4.7B at Q4_K_M, ~21.5 tok/s) fits but uses 76% of memory, so keep context short
  • Gemma 4 E2B IT (5.1B at Q4_K_M, ~20.4 tok/s) fits but uses 78% of memory, so keep context short
  • FrogNano 4B 2609 (4.7B at Q4_K_M, ~21.5 tok/s) fits but uses 76% of memory, so keep context short
  • 14B-class models need ~10 GB at Q4_K_M, so they need CPU offload or more memory
  • 32B-class models need ~22 GB at Q4_K_M, so they need CPU offload or more memory

Mac Mini M2 (8 GB) Specifications

Brand
Apple
Chip
M2
Type
Mini PC
Unified Memory
8 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 4B on Q4_K_M; this is our top choice here.

$curl -fsSL https://ollama.com/install.sh | sh
$ollama run hf.co/unsloth/Qwen3.5-4B-GGUF:Q4_K_M

LM Studio

LM Studio

Download LM Studio, search for "Qwen3.5 4B", and run it with one click.

Prompt Processing

Estimated for Qwen3.5 4B, the compute-bound phase that reads your prompt before the first reply token appears.

153.7tok/s prefill

Short chat

3.3 s

512 tok prompt

Long chat

26.6 s

4,096 tok prompt

Document / codebase

213 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 (8 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.

  • Bigger models: step up to 12 GB

    iPhone 17 Pro Max

    Apple

    • 4 GB more memory
    • 23% lower memory bandwidth

    Unlocks 44 more official models, incl. GLM 4.1V 9B Thinking (10.3B at Q4_K_S).

    Open iPhone 17 Pro Max

Frequently Asked Questions

Can Mac Mini M2 (8 GB) run Qwen3.5 4B?

Yes. The Mac Mini M2 (8 GB) with 8 GB unified memory can run Qwen3.5 4B (Q4_K_M, 3.4 GB, 76% of memory). It is also the table's best pick, at ~21.5 tok/s. 36 of the 143 featured models get a Fit of S (runs great) here. The largest featured model that fits at Q4 or better is Baichuan2 7B Chat (7B at IQ4_XS). 14B-class models need around 10 GB at Q4_K_M, meaning they need CPU offloading or extra memory beyond what's available in 4.5 GB. Check the compatibility table above for the full list with Fit, VRAM usage and estimated speed. Models are checked against the 4.5 GB available to AI (that is, the 8 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 (8 GB)?

The Mac Mini M2 (8 GB) has 8 GB unified memory. After macOS reserves ~3.5 GB for the operating system, approximately 4.5 GB is available for AI models. Model rankings here use that 4.5 GB limit instead of the total 8 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 (8 GB) good for AI?

With 8 GB unified memory and 100.0 GB/s bandwidth, the Mac Mini M2 (8 GB) is good for running local AI models. Models that run well include 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); LFM2.5 2.6B (2.7B at Q4_K_M, ~34.3 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 (8 GB)?

For the Mac Mini M2 (8 GB), the best pick right now is Qwen3.5 4B at ~21.5 tok/s. Also running well: 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); LFM2.5 2.6B (2.7B at Q4_K_M, ~34.3 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 (8 GB) for AI inference?

With 100.0 GB/s memory bandwidth, the Mac Mini M2 (8 GB) achieves approximately 16 tok/s on a 7B model at Q4_K_M — that's functional for interactive use. In the ranking, the top pick Qwen3.5 4B runs at ~21.5 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 (8 GB)

~22 tok/s
~20 tok/s
~37 tok/s
~40 tok/s

Real-world results typically within ±20%.

Learn more about tok/s estimation →

Can I run AI offline on Mac Mini M2 (8 GB)?

Yes — once you download a model, it runs entirely on the Mac Mini M2 (8 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.