Macs
Which AI models run on the M1 (16 GB)?
The M1 (16 GB) has 16 GB of memory, about 10.7 GB of it usable for a model, and 68 GB/s of memory bandwidth. 43 of the 110 models in our catalog run on it at 4-bit or better.
Data as of 2 November 2026 · Vendor specs
Models that run on it
| Model | Best build | Memory | Speed |
|---|---|---|---|
| Nemotron Nano 9B v29B | Q4_K_M | 6.6 GB | ~6.9 tok/s slow our est. |
| Qwen3.5 9B Instruct9B | Q4_K_M | 8.2 GB | ~9.9 tok/s slow ModelFit est. |
| Qwen2.5 7B Instruct7B | Q4_K_M | 6.6 GB | ~11 tok/s ModelFit est. |
| Gemma 3 4B Instruct4B | Q8_0 | 5.0 GB | ~12 tok/s ModelFit est. |
| Ministral 3 14B13.5B | Q4_K_M | 10.3 GB | ~5.4 tok/s slow ModelFit est. |
| Mellum2.1 12B-A2.5B Thinking12.1B MoE | Q4_K_M | 9.3 GB | ~22 tok/s our est. |
| Gemma 4 12B12B | Q4_K_M | 9.2 GB | ~6.3 tok/s slow ModelFit est. |
| GLM-4-9B-Chat9.4B | Q4_K_M | 7.5 GB | ~8.4 tok/s slow ModelFit est. |
| Gemma 2 9B Instruct9B | Q4_K_M | 8.2 GB | ~7.0 tok/s slow ModelFit est. |
| Ornith 1.0 9B9B | Q4_K_M | 6.7 GB | ~9.9 tok/s slow ModelFit est. |
| MiMo-V2.6-Distill-Qwen-9B9B | Q4_K_M | 6.9 GB | ~9.6 tok/s slow ModelFit est. |
| Jebadiah 9B9B | Q4_K_M | 6.9 GB | ~9.7 tok/s slow ModelFit est. |
| Granite 4.2 8B8.8B | Q4_K_M | 6.1 GB | ~8.7 tok/s slow ModelFit est. |
| Granite Switch 4.1 8B8.8B | Q4_K_M | 7.6 GB | ~7.1 tok/s slow ModelFit est. |
| MiniCPM-V 4.5 8B8.7B | Q4_K_M | 6.8 GB | ~9.6 tok/s slow ModelFit est. |
| Ministral 3 8B8.4B | Q4_K_M | 7.1 GB | ~8.1 tok/s slow ModelFit est. |
| LFM2.5 8B-A1B8.3B MoE | Q4_K_M | 6.6 GB | ~29 tok/s ModelFit est. |
| Llama 3.1 8B Instruct8B | Q4_K_M | 7.7 GB | ~9.8 tok/s slow ModelFit est. |
| Qwen3 8B8B | Q4_K_M | 7.7 GB | ~9.2 tok/s slow ModelFit est. |
| Granite 4.1 8B Instruct8B | Q4_K_M | 6.6 GB | ~8.4 tok/s slow ModelFit est. |
| Mistral 7B Instruct7B | Q4_K_M | 6.6 GB | ~10 tok/s ModelFit est. |
| Qwen2.5 Coder 7B7B | Q4_K_M | 6.6 GB | ~11 tok/s ModelFit est. |
| DeepSeek-R1 Distill Qwen 7B7B | Q4_K_M | 6.6 GB | ~11 tok/s ModelFit est. |
| Gemma 4 E4B4.5B | Q8_0 | 8.7 GB | ~10 tok/s ModelFit est. |
| Qwen3.5 4B Instruct4B | Q8_0 | 5.4 GB | ~11 tok/s ModelFit est. |
| NVIDIA Nemotron 3.5 Bolt E4B4B | Q4_K_M | 9.1 GB | ~13 tok/s ModelFit est. |
| Phi-3 Mini 3.8B3.8B | Q4_K_M | 4.3 GB | ~12 tok/s our est. |
| Phi-4 Mini 3.8B3.8B | Q8_0 | 4.9 GB | ~11 tok/s ModelFit est. |
| Granite 4.2 3B3.7B | Q4_K_M | 3.2 GB | ~20 tok/s ModelFit est. |
| Ministral 3 3B3.4B | Q4_K_M | 4.1 GB | ~15 tok/s ModelFit est. |
| Qwen2.5 3B Instruct3B | Q4_K_M | 3.6 GB | ~24 tok/s ModelFit est. |
| Llama 3.2 3B Instruct3B | Q4_K_M | 3.6 GB | ~20 tok/s ModelFit est. |
| Granite 4.1 3B Instruct3B | Q4_K_M | 3.0 GB | ~20 tok/s ModelFit est. |
| Gemma 4 E2B2.3B | Q8_0 | 5.7 GB | ~24 tok/s ModelFit est. |
| Gemma 2 2B Instruct2B | Q4_K_M | 2.8 GB | ~23 tok/s ModelFit est. |
| Qwen3.5 2B Instruct2B | Q4_K_M | 2.8 GB | ~24 tok/s ModelFit est. |
| LFM3 1.7B1.73B | Q4_K_M | 2.1 GB | ~41 tok/s ModelFit est. |
| Qwen2.5 1.5B Instruct1.5B | Q4_K_M | 2.5 GB | ~43 tok/s ModelFit est. |
| Llama 3.2 1B Instruct1B | Q4_K_M | 2.0 GB | ~49 tok/s ModelFit est. |
| Gemma 3 1B Instruct1B | Q8_0 | 2.0 GB | ~43 tok/s ModelFit est. |
| Qwen3.5 0.8B Instruct0.8B | Q4_K_M | 1.8 GB | ~54 tok/s ModelFit est. |
| Qwen2.5 0.5B Instruct0.5B | Q4_K_M | 1.8 GB | ~94 tok/s ModelFit est. |
| SmolLM2 360M0.36B | Q4_K_M | 1.5 GB | ~52 tok/s ModelFit est. |
Speeds come from ModelFit's estimates (CC BY 4.0) where it covers the model, otherwise from our bandwidth formula. Method
Too big for this machine
Mistral Nemo 12B (10.7 GB), Gemma 3 12B Instruct (10.7 GB), Qwen2.5 14B Instruct (12.3 GB), Phi-3 Medium 14B (12.3 GB), Qwen2.5 Coder 14B (12.3 GB), Qwen3 14B (12.3 GB), DeepSeek-R1 Distill Qwen 14B (12.3 GB), Phi-4 14B (12.8 GB), gpt-oss-20b (15.1 GB), LFM2 24B-A2B Instruct (15.3 GB), Mistral Small 3.1 (16.4 GB), Devstral Small 2 24B (16.6 GB) and more.
Questions
What is the best AI model for the M1 (16 GB)?
Qwen2.5 7B Instruct at Q4_K_M: the largest model on our shortlist of current models that runs at 4-bit or better at a usable speed (about 11 tok/s, ModelFit est.).
How much memory can a model use on the M1 (16 GB)?
About 10.7 GB of the 16 GB.
How fast is the M1 (16 GB) for local LLMs?
Decode speed is limited by memory bandwidth: 68 GB/s. A model that reads 10 GB per token runs at under 7 tokens per second.