1The specs that matter for AI
| Unified memory | 24 GB |
|---|---|
| Memory bandwidth | 100 GB/s |
| Found in | MacBook Air (2022, 2023), 13-inch MacBook Pro (2022), Mac mini (2023) |
| Dedicated seat for it | Core seat: mines up to 20 MLX a day |
2Which models fit
At 4 bits per weight a model needs a little over half a gigabyte per billion parameters, plus room for its context and macOS. With 24 GB, the M2 comfortably holds models up to about 32 billion parameters.
| Model at 4-bit | Memory it needs | Fits on the M2 |
|---|---|---|
| 8B model | 4.5 GB | Yes |
| 14B model | 7.9 GB | Yes |
| 32B model | 18 GB | Yes |
| 70B model | 39.4 GB | No |
| gpt-oss-120b, mixture-of-experts | 65 GB | No |
| Qwen3.5-397B-A17B, mixture-of-experts | 223.3 GB | No |
| DeepSeek-V4.1-Flash, mixture-of-experts | 316 GB | No |
3How fast it runs
Writing text is limited by memory bandwidth: every new token reads the model's active weights once. Dividing the M2's 100 GB/s by the bytes read per token gives the ceiling below. Real runs usually reach about 45% to 80% of it (llama.cpp benchmarks).
| Model at 4-bit | Read per token | Ceiling for one chat |
|---|---|---|
| 8B model | 4.5 GB | 22 tokens/s |
| 14B model | 7.9 GB | 13 tokens/s |
| 32B model | 18 GB | 6 tokens/s |
5Compare with other chips
6Questions
- Can the M2 run a 70B model?
- Not comfortably. A 70B model at 4-bit needs about 40 GB, and the M2 has at most 24 GB. Models up to about 32B parameters fit well.
- How many tokens per second does the M2 get?
- For an 8B model at 4-bit, the ceiling is about 22 tokens a second, and real runs reach roughly half to four fifths of it. Bigger models are proportionally slower.
- Is the M2 good for AI?
- For running models locally, memory and bandwidth matter most. The M2 has up to 24 GB at 100 GB/s, enough for models up to about 32 billion parameters. It can also earn by running AI work for MLX Network with MLX Node.
Sources
- Apple: M2apple.com
- llama.cpp: Apple Silicon benchmarksgithub.com