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What can an M3 Max with 96 GB run?

38 of 73 open models run well on it. The GPU can use about 72 GB of its 96 GB by default, and it reads memory at 300 GB/s, which sets how fast it writes. Found in: MacBook Pro (14-inch, M3 Pro or M3 Max, Nov 2023), MacBook Pro (16-inch, Nov 2023).

Smartest that runs wellNemotron 3.5 Bolt 74B A9B6-bit · 60.1 GB · 18–33 tok/sBiggest that runs wellgpt-oss-120bnative 4.25-bit · 65.3 GB · 50–89 tok/sFor codeQwen3 Coder Next6-bit · 65 GB · 56–100 tok/s

Runs well 43

Fits with room for context, at 10 or more tokens a second.

ModelVersionMemoryTokens/s
Nemotron 3.5 Bolt 74B A9B74B · 9B active · Intelligence 21.46-bit60.1 GB18–33
Qwen3.6 35B A3B35B · 3B active · Intelligence 18.28-bit37.2 GB42–75
Nemotron 3.5 Lightning 30B A3B30B · 3B active · Intelligence 12.98-bit31.9 GB42–75
MiniCPM5 2B2.52B · Intelligence 12.58-bit2.7 GB50–89
gpt-oss-120b117B · 5.1B active · Intelligence 11.6native 4.25-bit65.3 GB50–89
Qwen3.5 9B9B · Intelligence 11.28-bit9.6 GB14–25
Granite 4.2 8B8B · Intelligence 11.18-bit8.5 GB16–28
Qwen3 Coder Next80B · 3B active · Intelligence 9.26-bit65 GB56–100
Granite 4.2 3B3B · Intelligence 9.18-bit3.2 GB42–75
gpt-oss-20b21B · 3.6B active · Intelligence 9native 4.25-bit13.8 GB71–126
Nemotron 3 Nano 30B A3B30B · 3.5B active · Intelligence 8.98-bit31.9 GB36–65
Gemma 4 E4B8B · Intelligence 8.98-bit8.5 GB28–50
Llama 4 Scout 17B-16E Instruct109B · 17B active · Intelligence 6.54-bit61.3 GB14–25
Ministral 3 8B8.9B · Intelligence 5.58-bit9.5 GB14–25
Nemotron 3 Nano 4B3.97B · Intelligence 4.88-bit4.2 GB32–57
GLM-4.5 Air106B · 12B active4-bit59.6 GB20–35
Qwen3 Coder 30B A3B Instruct30.5B · 3.3B active8-bit32.4 GB39–69
GLM-4.7 Flash30B · 3B active8-bit31.9 GB42–75
Gemma 4 26B A4B25.2B · 3.8B active8-bit26.8 GB34–60
Devstral Small 2 (24B)24B4-bit13.5 GB10–18
Magistral Small 1.2 (2509)24B4-bit13.5 GB10–18
Mistral Small 3.2 (24B)24B4-bit13.5 GB10–18
LFM2.5 24B A2B24B · 2B active8-bit25.5 GB64–114
Phi-4 Reasoning Vision 15B15B6-bit12.2 GB11–20
Phi-4 (14B)14B6-bit11.4 GB12–21
Gemma 4 12B Unified12B8-bit12.7 GB11–19
Qwen3 8B8.2B8-bit8.7 GB16–28
Llama 3.1 8B Instruct8B8-bit8.5 GB16–28
Gemma 4 E2B5.1B8-bit5.4 GB56–100
Qwen3.5 4B4B8-bit4.3 GB31–56
Phi-4 mini instruct3.8B8-bit4 GB34–60
Llama 3.2 3B Instruct3.21B8-bit3.4 GB40–71
SmolLM3 3B3B8-bit3.2 GB42–75
LFM2.5 2.6B2.69B8-bit2.9 GB47–83
Qwen3.5 2B2B8-bit2.1 GB64–114
Llama 3.2 1B Instruct1.23B8-bit1.3 GB104–185
Qwen3.5 0.8B0.8B8-bit0.9 GB150–267
Qwen3 0.6B0.6B8-bit0.6 GB225–400
Qwen3.8 Flash Next180B · 6B active · Intelligence 39.82-bit lower quality56.3 GB71–126
Qwen3.5 122B A10B122B · 10B active · Intelligence 15.63-bit lower quality53.4 GB31–55
Mistral Small 4 (119B)119B · 6.5B active · Intelligence 11.33-bit lower quality52.1 GB48–86
Ornith 1.5 122B122B · 10B active3-bit lower quality53.4 GB31–55
Nemotron 3 Super 120B A12B120B · 12B active3-bit lower quality52.5 GB25–45

Runs, but slowly 8

Fits, but under 10 tokens a second: fine for batch work, slow for chat.

ModelVersionMemoryTokens/s
Qwen3.8 27B27B · Intelligence 33.78-bit28.7 GB5–8
Muse Glimmer 30B29.6B · Intelligence 17.58-bit31.5 GB4–8
Gemma 4 31B30.7B · Intelligence 14.78-bit32.6 GB4–7
Granite 4.2 30B30B · Intelligence 12.78-bit31.9 GB4–8
Llama 3.3 70B Instruct70B6-bit56.9 GB2–4
Qwen3.6 27B27B8-bit28.7 GB5–8
Mistral Medium 3.5 (128B)128B · Intelligence 14.23-bit lower quality56 GB2–4
Devstral 2 (123B)123B3-bit lower quality53.8 GB3–4

Only just fits 1

Needs almost all memory: raise the GPU memory limit and keep the context short.

ModelVersionMemoryTokens/s
MiniMax M2.7230B · 10B active2-bit lower quality71.9 GB44–77

Too big 21

Needs more memory than 96 GB, even at 2 bits.

Other Macs

Every Mac with 96 GB, from any chip.

The M3 Max with more or less memory:

Neighbouring chips:

Head to head:

Compare it with any Mac or GPU · The M3 Max for local AI · The best Mac for local LLMs · Every Mac

Questions

What is the best LLM for an M3 Max with 96 GB?
Of the models with a published Intelligence Index, Nemotron 3.5 Bolt 74B A9B scores highest among those that run well here: 6-bit, about 18–33 tokens a second. The biggest that runs well is gpt-oss-120b.
Can an M3 Max with 96 GB run Llama 3.3 70B?
Yes, but slowly: 6-bit, at about 2–4 tokens a second.
How fast is gpt-oss-20b on an M3 Max?
About 71–126 tokens a second (native 4.25-bit), from the chip’s 300 GB/s memory bandwidth.

Run them with MLX Harness (free, open source) or Apple’s mlx-lm in Terminal: step by step.

How it’s worked out

Memory: the model’s parameters at 8, 6, 4, 3 or 2 bits per weight plus MLX’s group scales (half a bit per weight), or its native format, with room for the context on top. By default macOS lets the GPU use about 75% of unified memory. Speed: memory bandwidth divided by the bytes read per token is the ceiling for one conversation; in llama.cpp’s public Apple Silicon benchmarks real runs reach about 45% to 80% of it, which is the range shown. These are estimates, not measurements: measure your own Mac on MLX Bench. Model figures from each official model card, chip figures from Apple’s Tech Specs. Checked 2026-10-19. Same data as mlx.app/compare.

What LLMs can an M3 Max with 96 GB run? Models and speed