Apple M2 for local LLMs
Written by Jakub Rusinowski · Last updated
With 24 GB at 100 GB/s, the M2 runs 92 catalogued models at Q4_K_M with 8K context. The largest that fits is Ternary Bonsai 2 27B (~17.6 GB), and the top pick is LFM2.5-24B-A2B at 20–41 tok/s.
Up to 24 GB unified memory at 100 GB/s. Comfortably fits 7–8B models at Q4 with room for the OS. Common in the MacBook Air and Mac mini.
Models that run on the M2
Q4_K_M, 8K context, 18 GB usable. Ranked by quality and speed.
| Model | Memory · marker = 18 GB | VRAM | Speed |
|---|---|---|---|
| LFM2.5-24B-A2B LFM2.5 | 15.5 GB | 20–41 tok/s | |
| GPT-OSS 20B GPT-OSS | 13.6 GB | 14–29 tok/s | |
| Mellum2.1 12B Thinking Mellum2.1 | 9 GB | 20–41 tok/s | |
| Gemma 3n E2B Gemma 3n | 4.1 GB | 15–32 tok/s | |
| LFM2.5-8B-A1B LFM2.5 | 5.8 GB | 16–33 tok/s | |
| Trinity Mini Trinity | 16.9 GB | 17–35 tok/s | |
| Ternary Bonsai 2 4B Ternary Bonsai 2 | 3.6 GB | 12–25 tok/s | |
| Gemma 4 26B-A4B Gemma 4 | 17 GB | 12–24 tok/s | |
| Gemma 3n E4B Gemma 3n | 5.5 GB | 9.8–20 tok/s | |
| Llama 3.2 1B Instruct Llama 3.2 Family | 1.8 GB | 30–63 tok/s | |
| Llama 3.2 3B Instruct Llama 3.2 Family | 3.7 GB | 12–24 tok/s | |
| StarCoder 2 3B StarCoder 2 | 2.9 GB | 16–33 tok/s |
Buy it or rent the same memory
Buy the card, or rent a GPU with the same memory by the hour to try models first.
or compare on Vast.ai from $0.35/hr (typical low · varies)
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Speed vs other GPUs
Llama 3.1 8B, Q4_K_M. Estimated ranges. How this is calculated
Specifications
Specs last updated 2026-07-12.
- Memory
- 24 GB
- Memory bandwidth
- 100 GB/s
- Architecture
- ARM, 5nm TSMC
- Series
- Apple Silicon
- Board power
- 20 W
- Release year
- 2022
- Usable for models
- 18 GB (~75% of unified)
Similar GPUs
Frequently asked questions
Can the Apple M2 run local LLMs?
Yes. With 24 GB (18 GB usable by a model) it runs 92 of the catalogued models at Q4_K_M with 8K context; the largest is Ternary Bonsai 2 27B, needing about 17.6 GB.
How fast is the Apple M2 for AI inference?
It is estimated to run Llama 3.1 8B at 6.4–13 tok/s at Q4_K_M. Llama 3.3 70B does not fit: it needs about 44 GB against 18 GB usable. These are modelled estimates from memory bandwidth, not measurements; the methodology page shows the formula.
What LLMs can I run on 24 GB?
Among the best that fit: LFM2.5-24B-A2B, GPT-OSS 20B, Mellum2.1 12B Thinking, Gemma 3n E2B, LFM2.5-8B-A1B. The quickest start is Ollama: ollama run llama3.2:3b.
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Can I run it on the M2?
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