By memory size
| Memory | Run well | Biggest that runs well | gpt-oss-20b |
|---|---|---|---|
| 96 GB1,200 GB/s | 44 | gpt-oss-120b | 284–505 tok/s |
| 256 GB1,200 GB/s | 54 | MiMo V2.6 Flash | 284–505 tok/s |
| 512 GB1,200 GB/s | 61 | DeepSeek V4.1 Flash 1017 | 284–505 tok/s |
Models: Mac Studio (M5 Max or M5 Ultra). 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 M5 Ultra good for local LLMs?
- With 512 GB it runs 61 of 73 open models well, up to DeepSeek V4.1 Flash 1017, and writes about 284–505 tokens a second with gpt-oss-20b. The M5 Ultra reads memory at 1,200 GB/s, which sets how fast it writes.
- How much memory should a Mac Studio with M5 Ultra have for AI?
- With 96 GB it runs 44 open models well; with 512 GB, 61, 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 M4 Max
- Mac Studio M3 Ultra
- 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 M5 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.