The best local LLM for a 18GB Mac
M3 Pro 18GB — about 15.0 GiB usable once macOS has taken its share.
Unified memory is the whole game on Apple Silicon. Your 18GB is shared between macOS, your apps and the model, so the honest budget is closer to 15.0 GiB than to 18. Everything below is sized against that number, at Q4_K_M with an 8K context.
The picks
Best all-round: DeepSeek-R1-Distill-Qwen-14B
The largest general-purpose model that still leaves room to work. It loads in 11.2 GiB and generates around 13 tokens/sec on an M3 Pro 18GB. Full breakdown →
Best for coding: Qwen2.5-Coder 14B
Trained specifically on code, and worth the swap if that is your workload. It loads in 11.2 GiB and generates around 13 tokens/sec on an M3 Pro 18GB. Full breakdown →
Best for reasoning: DeepSeek-R1-Distill-Qwen-14B
Thinks before answering; slower per question, better on hard ones. It loads in 11.2 GiB and generates around 13 tokens/sec on an M3 Pro 18GB. Full breakdown →
Fastest usable: Llama 3.2 1B
When latency matters more than depth — voice assistants, autocomplete, agents. It loads in 1.8 GiB and generates around 158 tokens/sec on an M3 Pro 18GB. Full breakdown →
Everything that fits in 18GB
| Model | Params | Loaded | Tok/s | Max ctx |
|---|---|---|---|---|
| DeepSeek-R1-Distill-Qwen-14B | 14.8B | 11.2 GiB | 13 | 16K |
| Qwen2.5 14B | 14.8B | 11.2 GiB | 13 | 16K |
| Qwen2.5-Coder 14B | 14.8B | 11.2 GiB | 13 | 16K |
| Qwen3 14B | 14.8B | 10.9 GiB | 13 | 16K |
| Phi-4 14B | 14.7B | 11.2 GiB | 13 | 16K |
| Gemma 3 12B | 12.2B | 11.1 GiB | 16 | 16K |
| Gemma 2 9B | 9.24B | 9.0 GiB | 21 | 8K |
| Qwen3 8B | 8.2B | 6.8 GiB | 24 | 32K |
| Llama 3.1 8B | 8.03B | 6.6 GiB | 24 | 32K |
| DeepSeek-R1-Distill-Qwen-7B | 7.62B | 5.8 GiB | 26 | 64K |
| Qwen2.5 7B | 7.62B | 5.8 GiB | 26 | 64K |
| Qwen2.5-Coder 7B | 7.62B | 5.8 GiB | 26 | 64K |
| Mistral 7B v0.3 | 7.25B | 6.1 GiB | 27 | 32K |
| Gemma 3 4B | 4.3B | 4.4 GiB | 46 | 64K |
| Llama 3.2 3B | 3.21B | 3.6 GiB | 61 | 64K |
| Qwen2.5 3B | 3.09B | 2.9 GiB | 63 | 32K |
| Gemma 2 2B | 2.61B | 3.0 GiB | 75 | 8K |
| Qwen2.5 1.5B | 1.54B | 1.9 GiB | 127 | 32K |
| Llama 3.2 1B | 1.24B | 1.8 GiB | 158 | 128K |
What does not fit
| Model | Needs | Short by |
|---|---|---|
| Mistral Small 3 24B | 16.2 GiB | 1.2 GiB |
| Qwen3 30B-A3B | 19.8 GiB | 4.8 GiB |
| Gemma 2 27B | 20.0 GiB | 5.0 GiB |
| Gemma 3 27B | 21.1 GiB | 6.1 GiB |
| DeepSeek-R1-Distill-Qwen-32B | 22.4 GiB | 7.4 GiB |
| Qwen2.5 32B | 22.4 GiB | 7.4 GiB |
| Qwen2.5-Coder 32B | 22.4 GiB | 7.4 GiB |
| Qwen3 32B | 22.4 GiB | 7.4 GiB |
| Mixtral 8x7B | 29.8 GiB | 14.8 GiB |
| DeepSeek-R1-Distill-Llama-70B | 45.6 GiB | 30.6 GiB |
A model that is a gigabyte or two over can often be rescued by dropping to Q3_K_M or quantising the KV cache. Anything further over than that is better solved by picking a smaller model — a 14B at Q4 beats a 32B at Q2 on almost every task.