Mac Studio with M2 Max for local AI.

The M2 Max reads memory at 400 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.

32 GB38 models run wellQwen3.6 35B A3B · 106–188 tok/s64 GB39 models run wellNemotron 3.5 Bolt 74B A9B · 35–63 tok/s96 GB43 models run wellgpt-oss-120b · 67–119 tok/s

By memory size

MemoryRun wellBiggest that runs wellgpt-oss-20b
32 GB400 GB/s38Qwen3.6 35B A3B95–168 tok/s
64 GB400 GB/s39Nemotron 3.5 Bolt 74B A9B95–168 tok/s
96 GB400 GB/s43gpt-oss-120b95–168 tok/s

Models: Mac Studio (2023). Not every memory size is offered with every model; Apple’s Tech Specs list what each one comes with.

Questions

Is the Mac Studio with M2 Max good for local LLMs?
With 96 GB it runs 43 of 73 open models well, up to gpt-oss-120b, and writes about 95–168 tokens a second with gpt-oss-20b. The M2 Max reads memory at 400 GB/s, which sets how fast it writes.
How much memory should a Mac Studio with M2 Max have for AI?
With 32 GB it runs 38 open models well; with 96 GB, 43, up to gpt-oss-120b. Memory decides which models fit, and it can’t be upgraded later.

Other Macs

The Mac Studio with other chips:

Other Macs with the M2 Max:

Head to head:

The M2 Max 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.

Mac Studio M2 Max for local AI: which LLMs it runs, by memory