By memory size
| Memory | Run well | Biggest that runs well | gpt-oss-20b |
|---|---|---|---|
| 32 GB400 GB/s | 38 | Qwen3.6 35B A3B | 95–168 tok/s |
| 64 GB400 GB/s | 39 | Nemotron 3.5 Bolt 74B A9B | 95–168 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 Max good for local LLMs?
- With 64 GB it runs 39 of 73 open models well, up to Nemotron 3.5 Bolt 74B A9B, and writes about 95–168 tokens a second with gpt-oss-20b. The M1 Max reads memory at 400 GB/s, which sets how fast it writes.
- How much memory should a Mac Studio with M1 Max have for AI?
- With 32 GB it runs 38 open models well; with 64 GB, 39, up to Nemotron 3.5 Bolt 74B A9B. Memory decides which models fit, and it can’t be upgraded later.
Other Macs
The Mac Studio with other chips:
- Mac Studio M5 Max
- Mac Studio M5 Ultra
- Mac Studio M4 Max
- Mac Studio M3 Ultra
- Mac Studio M2 Max
- Mac Studio M2 Ultra
- Mac Studio M1 Ultra
Other Macs with the M1 Max:
Head to head:
The M1 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.