Mac › 16 GB
Best local LLMs for a 16GB Mac
Short answer: A 16GB Mac lets the GPU use about 11 GB by default (~67%). That fits Phi 4 reasoning vision 15B at IQ4_XS with room for 8k context. Best picks: Phi 4 reasoning vision 15B, Ministral 3 14B (2512), Gemma 4 12B. Memory figures are estimates. Speeds are measured llama.cpp results where we have them and labeled estimates otherwise.
Checked 2026-10-22 · memory figures are estimates; measured speeds link their source
| Model | Quant | Memory needed (estimated, 8k ctx) | Fits default limit | Speed on Apple M4 (16 GB) | Format |
|---|---|---|---|---|---|
| Phi 4 reasoning vision 15B | IQ4_XS | 10.7 GB | Y | ~11 tok/s est. | GGUF |
| Ministral 3 14B (2512) | IQ4_XS | 9.7 GB | Y | ~12 tok/s est. | GGUF |
| Gemma 4 12B | Q5_K_M | 10.4 GB | Y | ~11 tok/s est. | GGUF |
| GLM 4.6V Flash | Q6_K | 9.8 GB | Y | ~11 tok/s est. | GGUF |
| Qwen3.5 9B | Q6_K | 9.2 GB | Y | ~12 tok/s est. | GGUF |
| Ministral 3 8B (2512) | Q6_K | 9.4 GB | Y | ~12 tok/s est. | GGUF |
| granite 4.2 8b | Q6_K | 9.5 GB | Y | ~13 tok/s est. | GGUF |
| Qwen3 VL 8B | Q6_K | 9.4 GB | Y | ~13 tok/s est. | GGUF |
| LFM2.5 8B A1B (MoE) MoE | Q8_0 | 10.1 GB | Y | not measured | GGUF |
| Gemma 4 E4B | Q8_0 | 9.7 GB | Y | ~11 tok/s est. | GGUF |
| Gemma 4 E2B | Q8_0 | 6.4 GB | Y | ~17 tok/s est. | GGUF |
| Qwen3.5 4B | Q8_0 | 6.1 GB | Y | ~18 tok/s est. | GGUF |
| Qwen3 4B (2507) | Q8_0 | 6.3 GB | Y | ~21 tok/s est. | GGUF |
| Nemotron Labs Dory 1.0 4B | Q8_0 | 5.5 GB | Y | ~22 tok/s est. | GGUF |
| NVIDIA Nemotron 3 Nano 4B | Q8_0 | 5.2 GB | Y | ~22 tok/s est. | GGUF |
| Ministral 3 3B (2512) | Q8_0 | 5.8 GB | Y | ~22 tok/s est. | GGUF |
| Qwen3.5 2B | Q8_0 | 3.2 GB | Y | ~38 tok/s est. | GGUF |
Best quant = highest of Q8_0, Q6_K, Q5_K_M, Q4_K_M, IQ4_XS, Q3_K_M that fits the default limit, f16 KV cache, 8k context. Speeds are llama.cpp (GGUF) figures; we don't have MLX runs. Estimated speeds are scaled from measured Llama 2 7B runs on the same chip and are not done for MoE models.
Why your Mac shows less than 16 GB
macOS caps how much unified memory the GPU can wire. We assume about 67% (about 11 GB here), roughly 67% below 64 GB and 75% at 64 GB and up. You can raise the cap with sudo sysctl iogpu.wired_limit_mb=…, at your own risk; leave room for macOS and your apps. These shares are approximate. Method.
Which Macs have 16GB
Apple M1 (16 GB), Apple M1 Pro (16 GB), Apple M2 (16 GB), Apple M2 Pro (16 GB), Apple M3 (16 GB), Apple M4 (16 GB), Apple M5 (16 GB).
FAQ
Is 16GB enough to run an LLM on a Mac?
Yes. 17 of 64 current models in our list fit fully at Q3_K_M or better with 8k context, the largest being Phi 4 reasoning vision 15B at IQ4_XS (about 10.7 GB, estimate). Bigger models need a Mac with more memory.
How much of my Mac's memory can the GPU use?
By default macOS caps GPU wired memory at roughly 67% of total below 64 GB and roughly 75% at 64 GB and up, so about 11 GB on a 16GB Mac (approximate). The iogpu.wired_limit_mb sysctl can raise it, at your own risk.
Is MLX faster than llama.cpp on a Mac?
We haven't measured it. Public tests conflict: MLX often shows higher generation tok/s, but one M1 Max test found GGUF in llama.cpp finished short-output tasks faster because of prompt processing. See our MLX vs GGUF answer for the sources.
Can a 16GB Mac run a 70B model?
No, not at the default limit. Llama 3.3 70B Instruct needs about 48.2 GB at Q4_K_M with 8k context (estimate), more than the roughly 11 GB the GPU can use.
Mac or a used RTX 3090 for local LLMs?
It's a trade. The 3090 has 24 GB of VRAM and is usually faster on models that fit. A 16GB Mac gives the GPU about 11 GB, so it fits less than a 3090. Check both in the checker.
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