Best Mac for local LLMs in 2026
Mac Mini M4 Pro 48GB is the strongest broad starting answer right now, while MacBook Pro M4 Pro 48GB 14-inch is the portable step-up. Use the guide when your real query is broader than a single Mac page.
Best local LLMs for this Mac
Mac Studio M4 Max 64GB ranked for coding with a most capable bias, using the best available runtime evidence.
| Rank | Model | Score | Quant | Tok/s | Runtime | Evidence | Headroom | Why it ranks here |
|---|---|---|---|---|---|---|---|---|
| 1 | Qwen 3 32B | 274 | 8bit | 22.0 tok/s | Ollama | Estimated | 31.0 GB | 8bit is the highest practical quality here. 22.0 tok/s estimated from nearby benchmark coverage, with Ollama wrapper on llama.cpp as the best runtime hint. 31.0 GB headroom leaves workable context margin. |
| 2 | Devstral Small 1.1 | 262 | 8bit | 33.0 tok/s | LM Studio | Estimated | 39.9 GB | 8bit is the highest practical quality here. 33.0 tok/s estimated from nearby benchmark coverage, with LM Studio wrapper on mixed as the best runtime hint. 39.9 GB headroom leaves workable context margin. |
| 3 | Devstral Small 2 24B | 262 | 8bit | 23.4 tok/s | llama.cpp | Estimated | 39.9 GB | 8bit is the highest practical quality here. 23.4 tok/s estimated from nearby benchmark coverage, with llama.cpp backend as the best runtime hint. 39.9 GB headroom leaves workable context margin. |
| 4 | Qwen3.5-27B | 260 | 8bit | 16.1 tok/s | llama.cpp | Estimated | 36.4 GB | 8bit is the highest practical quality here. 16.1 tok/s estimated from nearby benchmark coverage, with llama.cpp backend as the best runtime hint. 36.4 GB headroom leaves workable context margin. |
| 5 | Gemma 3 27B | 257 | 8bit | 13.0 tok/s | LM Studio | Estimated | 34.1 GB | 8bit is the highest practical quality here. 13.0 tok/s estimated from nearby benchmark coverage, with LM Studio wrapper on mixed as the best runtime hint. 34.1 GB headroom leaves workable context margin. |
| 6 | DeepSeek R1 Distill Qwen 32B | 252 | 8bit | Measure it | Best available | Fit-first | 31.0 GB | 8bit is the highest practical quality here. Speed still needs direct measurement. 31.0 GB headroom leaves workable context margin. |
| 7 | Mistral Small 3.1 24B | 240 | 8bit | Measure it | Best available | Fit-first | 39.9 GB | 8bit is the highest practical quality here. Speed still needs direct measurement. 39.9 GB headroom leaves workable context margin. |
| 8 | Magistral Small | 240 | 8bit | Measure it | Best available | Fit-first | 39.9 GB | 8bit is the highest practical quality here. Speed still needs direct measurement. 39.9 GB headroom leaves workable context margin. |
| 9 | Llama 3.3 70B | 239 | 6bit | 8.2 tok/s | LM Studio | Estimated | 11.7 GB | 6bit is the highest practical quality here. 8.2 tok/s estimated from nearby benchmark coverage, with LM Studio wrapper on mixed as the best runtime hint. 11.7 GB headroom leaves workable context margin. |
| 10 | Nemotron-3-Nano-30B-A3B | 233 | 8bit | 43.7 tok/s | llama.cpp | Estimated | 35.2 GB | 8bit is the highest practical quality here. 43.7 tok/s estimated from nearby benchmark coverage, with llama.cpp backend as the best runtime hint. 35.2 GB headroom leaves workable context margin. |
| 11 | Qwen 3 30B-A3B | 233 | 8bit | 84.9 tok/s | MLX | Estimated | 34.3 GB | 8bit is the highest practical quality here. 84.9 tok/s estimated from nearby benchmark coverage, with MLX backend as the best runtime hint. 34.3 GB headroom leaves workable context margin. |
| 12 | Qwen3-Coder-30B-A3B | 233 | 8bit | 58.5 tok/s | llama.cpp | Estimated | 34.7 GB | 8bit is the highest practical quality here. 58.5 tok/s estimated from nearby benchmark coverage, with llama.cpp backend as the best runtime hint. 34.7 GB headroom leaves workable context margin. |
| 13 | GLM-4.7-Flash | 232 | 8bit | 58.0 tok/s | llama.cpp | Estimated | 28.2 GB | 8bit is the highest practical quality here. 58.0 tok/s estimated from nearby benchmark coverage, with llama.cpp backend as the best runtime hint. 28.2 GB headroom leaves workable context margin. |
| 14 | Qwen3.5-35B-A3B | 232 | 8bit | 57.6 tok/s | MLX | Estimated | 30.3 GB | 8bit is the highest practical quality here. 57.6 tok/s estimated from nearby benchmark coverage, with MLX backend as the best runtime hint. 30.3 GB headroom leaves workable context margin. |
| 15 | Qwen 3 8B | 211 | 8bit | 63.1 tok/s | LM Studio | Estimated | 54.7 GB | 8bit is the highest practical quality here. 63.1 tok/s estimated from nearby benchmark coverage, with LM Studio wrapper on mixed as the best runtime hint. 54.7 GB headroom leaves workable context margin. |
| 16 | Llama 2 7B | 209 | 8bit | 36.4 tok/s | llama.cpp | Estimated | 53.2 GB | 8bit is the highest practical quality here. 36.4 tok/s estimated from nearby benchmark coverage, with llama.cpp backend as the best runtime hint. 53.2 GB headroom leaves workable context margin. |
| 17 | Qwen3.5-9B | 206 | 8bit | 15.0 tok/s | llama.cpp | Estimated | 54.1 GB | 8bit is the highest practical quality here. 15.0 tok/s estimated from nearby benchmark coverage, with llama.cpp backend as the best runtime hint. 54.1 GB headroom leaves workable context margin. |
| 18 | Qwen 2.5 14B | 199 | 8bit | Measure it | Best available | Fit-first | 49.0 GB | 8bit is the highest practical quality here. Speed still needs direct measurement. 49.0 GB headroom leaves workable context margin. |
| 19 | Phi-4 14B | 198 | 8bit | Measure it | Best available | Fit-first | 49.5 GB | 8bit is the highest practical quality here. Speed still needs direct measurement. 49.5 GB headroom leaves workable context margin. |
| 20 | Llama 3.1 8B | 189 | 8bit | Measure it | Best available | Fit-first | 55.0 GB | 8bit is the highest practical quality here. Speed still needs direct measurement. 55.0 GB headroom leaves workable context margin. |
| 21 | Qwen 2.5 7B | 189 | 8bit | Measure it | Best available | Fit-first | 56.0 GB | 8bit is the highest practical quality here. Speed still needs direct measurement. 56.0 GB headroom leaves workable context margin. |
| 22 | Mistral 7B v0.3 | 188 | 8bit | Measure it | Best available | Fit-first | 55.7 GB | 8bit is the highest practical quality here. Speed still needs direct measurement. 55.7 GB headroom leaves workable context margin. |
| 23 | GLM-4.5-Air | 158 | MXFP4 | 18.0 tok/s | LM Studio | Estimated | 9.6 GB | MXFP4 is the highest practical quality here. 18.0 tok/s estimated from nearby benchmark coverage, with LM Studio wrapper on mixed as the best runtime hint. 9.6 GB headroom leaves workable context margin. |
| 24 | Qwen3-Coder-Next | 156 | Q5_K_M | 74.0 tok/s | MLX | Estimated | 9.8 GB | Q5_K_M is the highest practical quality here. 74.0 tok/s estimated from nearby benchmark coverage, with MLX backend as the best runtime hint. 9.8 GB headroom leaves workable context margin. |
| 25 | Gemma 3 4B | 150 | 8bit | 100.5 tok/s | LM Studio | Estimated | 58.4 GB | 8bit is the highest practical quality here. 100.5 tok/s estimated from nearby benchmark coverage, with LM Studio wrapper on mixed as the best runtime hint. 58.4 GB headroom leaves workable context margin. |
| 26 | Qwen 3 4B | 150 | 8bit | 143.2 tok/s | MLX | Estimated | 58.6 GB | 8bit is the highest practical quality here. 143.2 tok/s estimated from nearby benchmark coverage, with MLX backend as the best runtime hint. 58.6 GB headroom leaves workable context margin. |
| 27 | Qwen 3 0.6B | 145 | 8bit | 370.0 tok/s | LM Studio | Estimated | 62.8 GB | 8bit is the highest practical quality here. 370.0 tok/s estimated from nearby benchmark coverage, with LM Studio wrapper on mixed as the best runtime hint. 62.8 GB headroom leaves workable context margin. |
| 28 | Llama 3.2 1B | 124 | 8bit | Measure it | Best available | Fit-first | 62.1 GB | 8bit is the highest practical quality here. Speed still needs direct measurement. 62.1 GB headroom leaves workable context margin. |
Popular Buyer Paths
Search-led shortcuts beyond the ranking table
These links target the broad buyer queries already clustering around 16GB Macs, M5 Pro MacBook Pros, and the general "best Mac for local LLMs" search.
Best local LLMs for Apple Silicon 16GB Macs
The current coding-biased answers point to Devstral Small 2 24B on the MacBook Air M4 16GB and Devstral Small 2 24B on the Mac mini M4 16GB. Treat 16GB as the compact-model tier and verify exact fit before you buy.
M5 Pro MacBook Pro evidence
The current M5 Pro 64GB record covers 4 published rows across 4 tracked models, with the fastest published row at 41.9 tok/s on Qwen3.5-35B-A3B.
If the real decision is local Mac versus rented GPU economics, compare the hardware path in Worth with AI Datacenter Index.
Frequently asked questions
- Which Mac is best for running LLMs locally?
- Mac Mini M4 Pro 48GB is the strongest broad starting answer for value right now, while MacBook Pro M4 Pro 48GB 14-inch is the portable step-up when you need more headroom. The real winner still depends on the model class you need, your budget, and whether you care more about capability or responsiveness.
- Can a 16GB Mac run useful local LLMs?
- Yes. The current coding-biased answers point to Devstral Small 2 24B on the MacBook Air M4 16GB and Devstral Small 2 24B on the Mac mini M4 16GB. Treat 16GB as the compact-model tier rather than a blanket promise for larger 27B-class daily drivers, and verify exact fit before you buy.
- How much unified memory do you need for local LLMs on a Mac?
- Unified memory determines which quantization tier fits cleanly, how much context you can keep live, and whether a recommendation stays practical after launch. In practice, 16GB can be useful for compact models, 24GB to 48GB opens more working room, and 64GB-plus tiers are where larger frontier setups become more credible. Use Fit to verify the exact model and headroom instead of relying on RAM labels alone.
- What does tok/s mean and how much do I need?
- Tokens per second measures how fast a model generates text. Around 8 to 15 tok/s usually feels interactive, while heavier coding, agent, or batch workflows benefit from more. Silicon Score ranks Macs with measured or explicitly labeled estimated speed so you can trade responsiveness against model quality with the evidence visible.
- Is local LLM inference on a Mac cheaper than using an API?
- If you run daily, want predictable privacy, or repeatedly use the same model class, local hardware can make sense. If your usage is spiky or you expect to burst into much larger models, compare the Mac path against rented GPU economics on AI Datacenter Index before you over-buy hardware.
- What quantization format should I use on a Mac?
- GGUF remains a common path for llama.cpp and Ollama on Macs, but there is no single best quantization for every machine. The practical answer depends on memory ceiling, runtime support, and how much quality loss you can tolerate. Start from the quantization and runtime surfaced in Rankings and Bench instead of assuming one preset wins everywhere.