By memory size
| Memory | Run well | Biggest that runs well | gpt-oss-20b |
|---|---|---|---|
| 16 GB120 GB/s | 19 | Qwen3.5 9B | 34–60 tok/s |
| 24 GB120 GB/s | 22 | Gemma 4 26B A4B | 28–51 tok/s |
| 32 GB120 GB/s | 27 | Qwen3.6 35B A3B | 28–51 tok/s |
Models: iMac (24-inch, 2024, Two ports), iMac (24-inch, 2024, Four ports). Not every memory size is offered with every model; Apple’s Tech Specs list what each one comes with.
Questions
- Is the iMac with M4 good for local LLMs?
- With 32 GB it runs 27 of 73 open models well, up to Qwen3.6 35B A3B, and writes about 28–51 tokens a second with gpt-oss-20b. The M4 reads memory at 120 GB/s, which sets how fast it writes.
- How much memory should a iMac with M4 have for AI?
- With 16 GB it runs 19 open models well; with 32 GB, 27, up to Qwen3.6 35B A3B. Memory decides which models fit, and it can’t be upgraded later.
Other Macs
The iMac with other chips:
Other Macs with the M4:
Head to head:
The M4 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.