By memory size
| Memory | Run well | Biggest that runs well | gpt-oss-20b |
|---|---|---|---|
| 8 GB100 GB/s | 16 | Granite 4.2 8B | too big |
| 16 GB100 GB/s | 17 | Qwen3 8B | 28–50 tok/s |
| 24 GB100 GB/s | 20 | Gemma 4 26B A4B | 24–42 tok/s |
Models: MacBook Pro (14-inch, M3, Nov 2023). Not every memory size is offered with every model; Apple’s Tech Specs list what each one comes with.
Questions
- Is the MacBook Pro with M3 good for local LLMs?
- With 24 GB it runs 20 of 73 open models well, up to Gemma 4 26B A4B, and writes about 24–42 tokens a second with gpt-oss-20b. The M3 reads memory at 100 GB/s, which sets how fast it writes.
- How much memory should a MacBook Pro with M3 have for AI?
- With 8 GB it runs 16 open models well; with 24 GB, 20, up to Gemma 4 26B A4B. Memory decides which models fit, and it can’t be upgraded later.
Other Macs
The MacBook Pro with other chips:
- MacBook Pro M5
- MacBook Pro M5 Pro
- MacBook Pro M5 Max
- MacBook Pro M4
- MacBook Pro M4 Pro
- MacBook Pro M4 Max
- MacBook Pro M3 Pro
- MacBook Pro M3 Max
- MacBook Pro M2
- MacBook Pro M2 Pro
- MacBook Pro M2 Max
- MacBook Pro M1
- MacBook Pro M1 Pro
- MacBook Pro M1 Max
Other Macs with the M3:
Head to head:
The M3 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.