By memory size
| Memory | Run well | Biggest that runs well | gpt-oss-20b |
|---|---|---|---|
| 96 GB819 GB/s | 43 | gpt-oss-120b | 194–345 tok/s |
| 256 GB819 GB/s | 53 | MiMo V2.6 Flash | 194–345 tok/s |
| 512 GB819 GB/s | 60 | DeepSeek V4.1 Flash 1017 | 194–345 tok/s |
Models: Mac Studio (2025). 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 M3 Ultra good for local LLMs?
- With 512 GB it runs 60 of 73 open models well, up to DeepSeek V4.1 Flash 1017, and writes about 194–345 tokens a second with gpt-oss-20b. The M3 Ultra reads memory at 819 GB/s, which sets how fast it writes.
- How much memory should a Mac Studio with M3 Ultra have for AI?
- With 96 GB it runs 43 open models well; with 512 GB, 60, up to DeepSeek V4.1 Flash 1017. 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 M2 Max
- Mac Studio M2 Ultra
- Mac Studio M1 Max
- Mac Studio M1 Ultra
Head to head:
Against PCs and graphics cards:
The M3 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.