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Apple M3 Max (36GB)

The Apple M3 Max (36GB) uses a unified memory architecture. For reliable local LLM inference without OS memory pressure, this benchmark models a 75% usable allocation (27 GB) with 300 GB/s unified memory bandwidth.

Memory

36 GB

Usable Memory Ceiling

27 GB (75% usable budget)

Bandwidth

300 GB/s

Max Inference Class

32B Models (Q4)

TDP

115W

VRAM Scaling by Context Length

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

DeepSeek-R1 3B (3B)Mistral-7B-v0.1 (7.3B)Mistral-Small-2501 (24B)AliceAI Foundation 80B A3B Base (80B (3B active))

Precision:

Best Models to Run on Apple M3 Max (36GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on Apple M3 Max (36GB).

Sort by:

Context:

KV Cache:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#33

Qwen 3.8 27B

27B

INT4

16 tok/s

~25.4s

#48

Agnes 3.0 Flash

33B

INT4

13 tok/s

~30.9s

#72

K2 Horizon MoVA 36B A4B

36B (4B active)

INT4

66 tok/s

~5.0s

#83

Qwen3.5-27B

27B

INT4

16 tok/s

~25.4s

#92

Sarvam-30B

32B (2.4B active)

INT4

87 tok/s

~3.5s

#94

Qwen3.6 35B A3B

35B (3B active)

INT4

78 tok/s

~4.0s

#96

Muse Glimmer 30B

30B

INT4

14 tok/s

~28.2s

#100

Phi-4 Reasoning Plus

14B

INT4
Q8

21 tok/s

14 tok/s

~13.4s

~13.4s

#101

MiMo V2 Flash

15B (309B active)

INT4
Q8

1 tok/s

1 tok/s

~278.0s

~278.6s

#108

Gemma 4 26B A4B

25.2B (3.8B active)

INT4
Q8

71 tok/s

44 tok/s

~4.5s

~5.4s

#109

Gemma 4 12B

11.95B

INT4
Q8
FP16

24 tok/s

16 tok/s

8 tok/s

~11.4s

~11.5s

~13.5s

#113

Qwen3.5-9B

9B

INT4
Q8
FP16

42 tok/s

25 tok/s

13 tok/s

~8.7s

~8.7s

~8.7s

#114

Qwen3.5-35B-A3B

35B (3B active)

INT4

78 tok/s

~4.0s

#118

K2 Horizon 7B

7B

INT4
Q8
FP16

52 tok/s

31 tok/s

17 tok/s

~6.8s

~6.8s

~6.8s

#128

NVIDIA Nemotron 3 Nano 30B-A3B

3.5B (30B active)

INT4
Q8
FP16

14 tok/s

8 tok/s

4 tok/s

~27.3s

~27.3s

~27.4s

#129

Gemma 4 31B

30.7B

INT4

14 tok/s

~28.8s

#131

Nemotron 3.5 Lightning

30B (3B active)

INT4

28 tok/s

~4.3s

#132

Qwen3.5-4B

4B

INT4
Q8
FP16

79 tok/s

51 tok/s

28 tok/s

~4.0s

~4.0s

~4.0s

#133

Qwen3-30B-A3B

30B (3B active)

INT4

79 tok/s

~4.0s

#136

GPT-OSS 20B

21B (3.6B active)

INT4
Q8

33 tok/s

26 tok/s

~4.3s

~5.0s

Inference Capacity

Maximum model parameter class runnable by weight precision.

Context:

4-bit

Q4

32B Models

8-bit

Q8

14B Models

16-bit

FP16

8B Models

Fine-Tuning Capacity

Hardware capacity for local training and adapter fine-tuning.

Context Length:

QLoRA

4-bit

32B Models

LoRA

16-bit

14B Models

Full Parameter Training

3B Models

Workload Recommendations

Guidance for optimal precision and operational ceilings on Apple M3 Max (36GB).

Inference Sweet Spot

Optimized for 14B models at uncompressed or Q8 precision, and 32B models at Q4 with up to 32k context.

Bandwidth & Speed Profile

With 300 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 67 tok/s on an 8B Q4 model and 8 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