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Apple M2 Max (96GB)

The Apple M2 Max (96GB) uses a unified memory architecture. For reliable local LLM inference without OS memory pressure, this benchmark models a 75% usable allocation (72 GB) with 400 GB/s unified memory bandwidth.

Memory

96 GB

Usable Memory Ceiling

72 GB (75% usable budget)

Bandwidth

400 GB/s

Max Inference Class

70B+ Models (Q4)

TDP

100W

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 14B (14B)Nemotron 3.5 Lightning 30B A3B (30B (3B active))Llama 3.1 70B (70B)Step 3.5 Flash (196.81B (11B active))

Precision:

Best Models to Run on Apple M2 Max (96GB)

Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on Apple M2 Max (96GB).

Sort by:

Context:

KV Cache:

RankModelParametersPrecisionTPS (Approx.)TTFTCan Run

#33

Qwen 3.8 27B

27B

INT4
Q8
FP16

21 tok/s

12 tok/s

6 tok/s

~19.9s

~19.9s

~20.0s

#48

Agnes 3.0 Flash

33B

INT4
Q8
FP16

17 tok/s

10 tok/s

5 tok/s

~24.2s

~24.3s

~26.5s

#61

Sarvam-105B

106B (10.3B active)

INT4

47 tok/s

~10.2s

#72

K2 Horizon MoVA 36B A4B

36B (4B active)

INT4
Q8
FP16

82 tok/s

58 tok/s

31 tok/s

~3.9s

~3.9s

~4.6s

#75

Ling 3.0 Flash VL

124B (5.5B active)

INT4

70 tok/s

~7.9s

#83

Qwen3.5-27B

27B

INT4
Q8
FP16

21 tok/s

12 tok/s

6 tok/s

~19.9s

~19.9s

~20.0s

#84

Qwen3.5-122B-A10B

122B (10B active)

INT4

35 tok/s

~11.3s

#92

Sarvam-30B

32B (2.4B active)

INT4
Q8
FP16

106 tok/s

80 tok/s

49 tok/s

~2.7s

~2.7s

~3.0s

#94

Qwen3.6 35B A3B

35B (3B active)

INT4
Q8
FP16

95 tok/s

70 tok/s

39 tok/s

~3.2s

~3.2s

~3.7s

#96

Muse Glimmer 30B

30B

INT4
Q8
FP16

19 tok/s

11 tok/s

5 tok/s

~22.1s

~22.1s

~24.1s

#100

Phi-4 Reasoning Plus

14B

INT4
Q8
FP16

27 tok/s

18 tok/s

10 tok/s

~10.5s

~10.5s

~10.5s

#101

MiMo V2 Flash

15B (309B active)

INT4
Q8
FP16

2 tok/s

1 tok/s

1 tok/s

~217.7s

~218.1s

~219.1s

#106

GLM-4.5-Air

106B (12B active)

INT4

12 tok/s

~13.4s

#108

Gemma 4 26B A4B

25.2B (3.8B active)

INT4
Q8
FP16

88 tok/s

61 tok/s

36 tok/s

~3.6s

~3.6s

~3.6s

#109

Gemma 4 12B

11.95B

INT4
Q8
FP16

31 tok/s

21 tok/s

12 tok/s

~9.0s

~9.0s

~9.0s

#113

Qwen3.5-9B

9B

INT4
Q8
FP16

53 tok/s

33 tok/s

17 tok/s

~6.8s

~6.8s

~6.8s

#114

Qwen3.5-35B-A3B

35B (3B active)

INT4
Q8
FP16

95 tok/s

70 tok/s

39 tok/s

~3.2s

~3.2s

~3.7s

#114

Ling 3.0 Flash

124B (5.1B active)

INT4

74 tok/s

~7.6s

#118

K2 Horizon 7B

7B

INT4
Q8
FP16

65 tok/s

41 tok/s

22 tok/s

~5.3s

~5.3s

~5.3s

#126

Qwen3 Next 80B A3B

80B (79B active)

INT4

6 tok/s

~57.7s

Inference Capacity

Maximum model parameter class runnable by weight precision.

Context:

4-bit

Q4

70B+ Models

8-bit

Q8

32B Models

16-bit

FP16

32B Models

Fine-Tuning Capacity

Hardware capacity for local training and adapter fine-tuning.

Context Length:

QLoRA

4-bit

70B+ Models

LoRA

16-bit

70B Models

Full Parameter Training

7B-8B Models

Workload Recommendations

Guidance for optimal precision and operational ceilings on Apple M2 Max (96GB).

Inference Sweet Spot

Suitable for 70B parameter models at Q4/Q8 with extended 32k context windows, or high-throughput batching of smaller dense architectures.

Bandwidth & Speed Profile

With 400 GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth bound regime. Generates approximately 89 tok/s on an 8B Q4 model and 11 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