Mac Studio with M1 Ultra for local AI.

The M1 Ultra reads memory at 800 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.

64 GB39 models run wellNemotron 3.5 Bolt 74B A9B · 71–125 tok/s128 GB47 models run wellQwen3.5 122B A10B · 64–114 tok/s

By memory size

MemoryRun wellBiggest that runs wellgpt-oss-20b
64 GB800 GB/s39Nemotron 3.5 Bolt 74B A9B189–337 tok/s
128 GB800 GB/s47Qwen3.5 122B A10B189–337 tok/s

Models: Mac Studio (2022). 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 M1 Ultra good for local LLMs?
With 128 GB it runs 47 of 73 open models well, up to Qwen3.5 122B A10B, and writes about 189–337 tokens a second with gpt-oss-20b. The M1 Ultra reads memory at 800 GB/s, which sets how fast it writes.
How much memory should a Mac Studio with M1 Ultra have for AI?
With 64 GB it runs 39 open models well; with 128 GB, 47, up to Qwen3.5 122B A10B. Memory decides which models fit, and it can’t be upgraded later.

Other Macs

The Mac Studio with other chips:

Head to head:

The M1 Ultra 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 M1 Ultra for local AI: which LLMs it runs, by memory