Is the M2 Max faster than the M2 Pro for LLMs?

The M2 Max reads memory about 100% faster than the M2 Pro (400 vs 200 GB/s), so it writes about 100% faster with any model that fits both. Reading a long prompt depends on GPU compute instead, which this comparison doesn’t cover.

Which can run bigger models, the M2 Pro or the M2 Max?

The M2 Max: The M2 Max goes up to 96 GB of memory, the M2 Pro to 32 GB. At its largest it runs 43 of 73 open models well, against 30.

M2 Pro vs M2 Max for local LLMs: memory, speed, which to buy

Side by side

	M2 Pro	M2 Max
Memory sizes	16, 32 GB	32, 64, 96 GB
Memory bandwidth	200 GB/s	400 GB/s
For models, at most	about 24 GB	about 72 GB
Models that run well	30 of 73	43 of 73
MLX seat class	Pro	Pro
Found in	MacBook Pro (14-inch, 2023), MacBook Pro (16-inch, 2023), Mac mini (2023)	MacBook Pro (14-inch, 2023), MacBook Pro (16-inch, 2023), Mac Studio (2023)

Speed with popular models

Tokens a second for one conversation, each chip at its largest memory size (32 GB and 96 GB), with the best version of the model that fits.

Model	M2 Pro	M2 Max
Llama 3.1 8B Instruct8B	11–19 8-bit	21–38 8-bit
gpt-oss-20b21B · 3.6B active	47–84 native 4.25-bit	95–168 native 4.25-bit
Qwen3.6 35B A3B35B · 3B active	53–94 4-bit	56–100 8-bit
Qwen3.8 27B27B	4–7 6-bit	12–21 4-bit
Llama 3.3 70B Instruct70B	4–7 2-bit lower quality	3–6 6-bit
gpt-oss-120b117B · 5.1B active	too big	67–119 native 4.25-bit
Qwen3.5 122B A10B122B · 10B active	too big	41–73 3-bit lower quality
DeepSeek V4 Flash 0731284B · 13B active	too big	too big

Questions

Is the M2 Max faster than the M2 Pro for LLMs?: The M2 Max reads memory about 100% faster than the M2 Pro (400 vs 200 GB/s), so it writes about 100% faster with any model that fits both. Reading a long prompt depends on GPU compute instead, which this comparison doesn’t cover.
Which can run bigger models, the M2 Pro or the M2 Max?: The M2 Max: The M2 Max goes up to 96 GB of memory, the M2 Pro to 32 GB. At its largest it runs 43 of 73 open models well, against 30.

More comparisons

The M2 Pro for local AI · The M2 Max for local AI · Compare every model · The best Mac for local LLMs

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.