MacBook Pro with M2 Pro for local AI.

The M2 Pro reads memory at 200 GB/s, which sets how fast it writes. Memory decides which models fit, and it can’t be upgraded later, so pick the size here.

16 GB22 models run wellPhi-4 Reasoning Vision 15B · 11–19 tok/s32 GB30 models run wellQwen3.6 35B A3B · 53–94 tok/s

By memory size

MemoryRun wellBiggest that runs wellgpt-oss-20b
16 GB200 GB/s22Phi-4 Reasoning Vision 15B56–100 tok/s
32 GB200 GB/s30Qwen3.6 35B A3B47–84 tok/s

Models: MacBook Pro (14-inch, 2023), MacBook Pro (16-inch, 2023). Not every memory size is offered with every model; Apple’s Tech Specs list what each one comes with.

Questions

Is the MacBook Pro with M2 Pro good for local LLMs?
With 32 GB it runs 30 of 73 open models well, up to Qwen3.6 35B A3B, and writes about 47–84 tokens a second with gpt-oss-20b. The M2 Pro reads memory at 200 GB/s, which sets how fast it writes.
How much memory should a MacBook Pro with M2 Pro have for AI?
With 16 GB it runs 22 open models well; with 32 GB, 30, up to Qwen3.6 35B A3B. Memory decides which models fit, and it can’t be upgraded later.

Other Macs

The MacBook Pro with other chips:

Other Macs with the M2 Pro:

Head to head:

The M2 Pro for local AI · The best Mac for local LLMs · Every Mac

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.

MacBook Pro M2 Pro for local AI: which LLMs it runs, by memory