MacBook Pro with M3 for local AI.

The M3 reads memory at 100 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.

8 GB16 models run wellGranite 4.2 8B · 10–18 tok/s16 GB17 models run wellQwen3 8B · 10–17 tok/s24 GB20 models run wellGemma 4 26B A4B · 21–38 tok/s

By memory size

MemoryRun wellBiggest that runs wellgpt-oss-20b
8 GB100 GB/s16Granite 4.2 8Btoo big
16 GB100 GB/s17Qwen3 8B28–50 tok/s
24 GB100 GB/s20Gemma 4 26B A4B24–42 tok/s

Models: MacBook Pro (14-inch, M3, Nov 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 M3 good for local LLMs?
With 24 GB it runs 20 of 73 open models well, up to Gemma 4 26B A4B, and writes about 24–42 tokens a second with gpt-oss-20b. The M3 reads memory at 100 GB/s, which sets how fast it writes.
How much memory should a MacBook Pro with M3 have for AI?
With 8 GB it runs 16 open models well; with 24 GB, 20, up to Gemma 4 26B A4B. Memory decides which models fit, and it can’t be upgraded later.

Other Macs

The MacBook Pro with other chips:

Other Macs with the M3:

Head to head:

The M3 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 M3 for local AI: which LLMs it runs, by memory