GPU GUIDE · APPLE

Best AI models for the
Apple M2 (16GB)

The Apple M2 (16GB) has 16.0GB of unified memory. Below are the top 30 open-source AI models that fit, ranked by composite benchmark score. Each row shows the best quantization that fits your hardware.

VRAM
16.0GB
Brand
apple
Models that fit
30
Generation
M2
Open full results filter →

Top 30 models for the Apple M2 (16GB)

01

Qwen 3.5 9B

9.0B
54.0
Best fit: Q8_0 · 10.6GBCan I run it? →
02

Qwen 3.5 4B

4.0B
45.1
Best fit: fp16 · 9.0GBCan I run it? →
03

GPT-OSS 20B

20.0B
40.8
Best fit: Q5_K_M · 15.2GBCan I run it? →
04

Devstral 2

24.0B
36.7
Best fit: Q4_K_M · 15.6GBCan I run it? →
05

Devstral Small 2 24B Instruct 2512

24.0B
32.4
Best fit: Q4_1 · 16.0GBCan I run it? →
06

Devstral Small 2

7.0B
31.7
Best fit: fp16 · 15.0GBCan I run it? →
07

gemma 4 E4B it

8.0B
31.3
Best fit: Q8_0 · 9.5GBCan I run it? →
08

Qwen3 4B Thinking 2507

4.0B
30.3
Best fit: fp16 · 9.0GBCan I run it? →
09

Qwen3 VL 8B Thinking

8.8B
27.8
Best fit: Q8_0 · 10.3GBCan I run it? →
10

DeepSeek R1 0528 Qwen3 8B

8.2B
27.4
Best fit: Q8_0 · 9.7GBCan I run it? →
11

Qwen 3.5 2B

2.0B
27.2
Best fit: fp16 · 5.0GBCan I run it? →
12

Qwen3 14B

14.8B
27.0
Best fit: Q6_K · 13.2GBCan I run it? →
13

Ministral 3 14B

14.0B
26.7
Best fit: Q8_0 · 15.9GBCan I run it? →
14

Ministral 3 14B 2512

13.9B
26.6
Best fit: Q8_0 · 15.8GBCan I run it? →
15

DeepSeek R1 Distill Qwen 14B

14.8B
26.4
Best fit: Q6_K · 13.2GBCan I run it? →
16

gemma 4 E2B it

5.1B
25.4
Best fit: fp16 · 11.2GBCan I run it? →
17

Ministral 3 8B

8.0B
25.0
Best fit: Q8_0 · 9.5GBCan I run it? →
18

Gemma 4 E2B

2.0B
25.0
Best fit: fp16 · 5.0GBCan I run it? →
19

Mistral Small 3.2 24B

24.0B
25.1
Best fit: Q4_1 · 16.0GBCan I run it? →
20

Nemotron Nano 12B 2 VL (free)

13.2B
24.8
Best fit: Q8_0 · 15.0GBCan I run it? →
21

Ministral 3 8B 2512

8.9B
24.7
Best fit: Q8_0 · 10.5GBCan I run it? →
22

Nemotron Nano 9B V2 (free)

8.9B
24.6
Best fit: Q8_0 · 10.5GBCan I run it? →
23

NVIDIA Nemotron 3 Nano 4B BF16

4.0B
24.5
Best fit: Q8_0 · 5.3GBCan I run it? →
24

Qwen3 VL 8B Instruct

8.8B
23.8
Best fit: Q8_0 · 10.3GBCan I run it? →
25

Qwen3 4B

4.0B
23.7
Best fit: Q8_0 · 5.3GBCan I run it? →
26

Qwen3 VL 4B Thinking

4.4B
22.9
Best fit: fp16 · 9.8GBCan I run it? →
27

Qwen3 8B

8.2B
22.0
Best fit: Q8_0 · 9.7GBCan I run it? →
28

Qwen3 4B Instruct 2507

4.0B
21.5
Best fit: fp16 · 9.0GBCan I run it? →
29

Mistral Small 3

23.6B
21.1
Best fit: Q4_1 · 15.8GBCan I run it? →
30

Granite 4.1 8B

8.8B
20.6
Best fit: Q8_0 · 10.3GBCan I run it? →
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FAQ — running AI on the Apple M2 (16GB)

How many AI models can the Apple M2 (16GB) run?

With 16.0GB of unified memory, the Apple M2 (16GB) can run 30+ open-source models from our database, including Qwen 3.5 9B, Qwen 3.5 4B, GPT-OSS 20B.

What's the largest LLM I can run on a Apple M2 (16GB)?

The biggest model that fits is approximately 24.0B. Larger models would need to be quantized further or won't fit at all.

Is 16.0GB of unified memory enough for local AI?

Yes for most use cases — 16.0GB runs 7B-13B class models at high quality. For 30B+ models you'll need to use heavy quantization or upgrade.