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Apple M3 Pro (18GB)

The Apple M3 Pro (18GB) uses a unified memory architecture. For reliable local LLM inference without OS memory pressure, this benchmark models a 75% usable allocation (13.5 GB) with 150 GB/s unified memory bandwidth.

Memory

18 GB

Usable Memory Ceiling

13.5 GB (75% usable budget)

Bandwidth

150 GB/s

Max Inference Class

14B Models (Q4)

TDP

75W

VRAM Scaling by Context Length

Estimated memory requirements as context length increases. Curves crossing above the reference line indicate Out of Memory (OOM).

Gemma 3 270M (0.27B)ChatGLM3-6B (6B)Sahabat-AI-Gemma2-9B (9.2B)Falcon-40B (40B)

Precision:

Best Models to Run on Apple M3 Pro (18GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on Apple M3 Pro (18GB).

Sort by:

Context:

KV Cache:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#100

Phi-4 Reasoning Plus

14B

INT4

10 tok/s

~24.2s

#101

MiMo V2 Flash

15B (309B active)

INT4

1 tok/s

~545.2s

#109

Gemma 4 12B

11.95B

INT4

12 tok/s

~19.1s

#113

Qwen3.5-9B

9B

INT4
Q8

23 tok/s

13 tok/s

~14.4s

~14.4s

#118

K2 Horizon 7B

7B

INT4
Q8

28 tok/s

17 tok/s

~11.3s

~11.3s

#128

NVIDIA Nemotron 3 Nano 30B-A3B

3.5B (30B active)

INT4
Q8
FP16

7 tok/s

4 tok/s

2 tok/s

~45.5s

~45.6s

~45.8s

#132

Qwen3.5-4B

4B

INT4
Q8
FP16

45 tok/s

28 tok/s

15 tok/s

~6.6s

~6.6s

~6.7s

#146

Granite 4.2 8B

8B

INT4
Q8

25 tok/s

15 tok/s

~12.8s

~12.9s

#150

Gemma 4 E4B

8B

INT4
Q8

25 tok/s

15 tok/s

~12.8s

~12.9s

#158

GLM-4V

9B

INT4
Q8

16 tok/s

10 tok/s

~14.4s

~15.7s

#160

Phi-4

14B

INT4

15 tok/s

~22.3s

#169

OLMo 3 7B Think

7B

INT4
Q8

19 tok/s

13 tok/s

~11.3s

~11.3s

#172

DeepSeek-R1 14B

14B

INT4

15 tok/s

~22.3s

#173

Ministral 3 14B

14B

INT4

10 tok/s

~24.2s

#177

Gemma 3 12B

12B

INT4
Q8

17 tok/s

9 tok/s

~19.1s

~22.6s

#181

Mistral-Small-2501

24B

INT4

8 tok/s

~44.4s

#182

Ministral 3 8B

8B

INT4
Q8

18 tok/s

11 tok/s

~12.9s

~14.0s

#183

Qwen3.5-2B

2B

INT4
Q8
FP16

75 tok/s

49 tok/s

28 tok/s

~3.5s

~3.5s

~3.5s

#184

MiniCPM5-2B

2B

INT4
Q8
FP16

75 tok/s

49 tok/s

28 tok/s

~3.5s

~3.5s

~3.5s

#185

Qwen2.5-14B

14B

INT4

15 tok/s

~22.3s

Inference Capacity

Maximum model parameter class runnable by weight precision.

Context:

4-bit

Q4

14B Models

8-bit

Q8

8B Models

16-bit

FP16

3B Models

Fine-Tuning Capacity

Hardware capacity for local training and adapter fine-tuning.

Context Length:

QLoRA

4-bit

14B Models

LoRA

16-bit

8B Models

Full Parameter Training

1B Models

Workload Recommendations

Guidance for optimal precision and operational ceilings on Apple M3 Pro (18GB).

Inference Sweet Spot

Optimal for 8B models across extended context (32k+). 14B models fit comfortably at Q4 precision with standard context.

Bandwidth & Speed Profile

With 150 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 33 tok/s on an 8B Q4 model and 4 tok/s on a 70B Q4 model.

Unified Memory Budget

Modeled with a 75% usable allocation budget to preserve system RAM for macOS and display compositor processes.

Frequently Asked Questions