Can I run it? / Mac with 16 GB
Best LLMs to run on a Mac with 16 GB
Updated November 2026
As of November 2026, the best model to run on a Mac with 16 GB is Ternary Bonsai 2 27B by PrismML (Small LM Index 71.2), at about 10 tokens/s with Q2_0 (as released) weights. For faster replies, LFM3 1.7B (61.2) runs at about 42 tokens/s.
A MacBook Air or Mac mini with 16 GB shares its memory between the system and the GPU; macOS lets the GPU use about two thirds of it, roughly 10–11 GB. About 11 GB is usable for a model, at 120 GB/s. The figures below assume the best quality that fits (8-bit down to 4-bit) and a 8K-token context, and only include models that generate at least 5 tokens per second.
Models that run well on a Mac with 16 GB
The 25 best of the 55 models that run at 5+ tokens/s, ranked by the Small LM Index.
| # | Model | Index | Memory | Speed |
|---|---|---|---|---|
| 1 | Ternary Bonsai 2 27B PrismML · Q2_0 (as released) · Apache-2.0 | 71.2 | 9.3 GB | ≈ 10 tok/s |
| 2 | Nanbeige4.2 3B Nanbeige (Kanzhun) · 8-bit · Apache-2.0 | 67.2 | 5.8 GB | ≈ 18 tok/s |
| 3 | Spark-X2.6 4B SparkLLM (XHToken) · 8-bit · Apache-2.0 | 66.5 | 5.3 GB | ≈ 18 tok/s |
| 4 | Spark-X2.5 4B SparkLLM (XHToken) · 8-bit · Apache-2.0 | 65.9 | 5.3 GB | ≈ 18 tok/s |
| 5 | Qwen3.5 9B Alibaba (Qwen) · 8-bit · Apache-2.0 | 64.1 | 11 GB | ≈ 8 tok/s |
| 6 | Granite 4.2 8B IBM (Granite) · 8-bit · Apache-2.0 | 62.7 | 11 GB | ≈ 9 tok/s |
| 7 | Ministral 3 14B Mistral AI · 4-bit · Apache-2.0 | 61.6 | 11 GB | ≈ 9 tok/s |
| 8 | LFM3 1.7B Liquid AI · 8-bit · LFM Open License | 61.2 | 2.5 GB | ≈ 42 tok/s |
| 9 | MiniCPM5 2B OpenBMB (MiniCPM) · 8-bit · Apache-2.0 | 60.9 | 3.6 GB | ≈ 29 tok/s |
| 10 | Mellum2.1 12B-A2.5B Thinking JetBrains (Mellum) · 5-bit · Apache-2.0 | 60.8 | 9.5 GB | ≈ 22 tok/s |
| 11 | Qwen3 4B Thinking 2507 Alibaba (Qwen) · 8-bit · Apache-2.0 | 59.8 | 6.0 GB | ≈ 18 tok/s |
| 12 | Qwen3.5 4B Alibaba (Qwen) · 8-bit · Apache-2.0 | 59.7 | 5.1 GB | ≈ 18 tok/s |
| 13 | Nemotron Labs Dory 1.0 4B NVIDIA · 8-bit · OpenMDW-1.1 | 58.6 | 5.2 GB | ≈ 18 tok/s |
| 14 | Falcon H1R 7B TII (Falcon) · 8-bit · Falcon LLM License | 58.6 | 9.1 GB | ≈ 10 tok/s |
| 15 | Gemma 4 12B Google · 5-bit · Apache-2.0 | 58.3 | 10 GB | ≈ 9 tok/s |
| 16 | Ministral 3 8B Mistral AI · 8-bit · Apache-2.0 | 57.7 | 10 GB | ≈ 9 tok/s |
| 17 | Granite 4.2 3B IBM (Granite) · 8-bit · Apache-2.0 | 57.3 | 4.4 GB | ≈ 24 tok/s |
| 18 | MiniCPM-SALA OpenBMB (MiniCPM) · 6-bit · Apache-2.0 | 56.9 | 8.7 GB | ≈ 10 tok/s |
| 19 | DeepSeek-R1-0528-Qwen3-8B DeepSeek · 8-bit · MIT | 56.3 | 11 GB | ≈ 9 tok/s |
| 20 | Hunyuan 7B Instruct Tencent (Hunyuan) · 8-bit · Tencent Hunyuan Community | 55.8 | 9.7 GB | ≈ 10 tok/s |
| 21 | Ling 3.0 Tiny InclusionAI (Ling) · 8-bit · MIT | 54.9 | 9.3 GB | ≈ 26 tok/s |
| 22 | Qwen3 8B Alibaba (Qwen) · 8-bit · Apache-2.0 | 53.9 | 11 GB | ≈ 9 tok/s |
| 23 | Ministral 3 3B Mistral AI · 8-bit · Apache-2.0 | 53.8 | 4.6 GB | ≈ 24 tok/s |
| 24 | Step3-VL-10B StepFun · 6-bit · Apache-2.0 | 53.2 | 10 GB | ≈ 10 tok/s |
| 25 | Nemotron Nano 9B v2 NVIDIA · 8-bit · NVIDIA Open Model | 52.9 | 10 GB | ≈ 8 tok/s |
Change the context length or quantization, or see models that only just fit, in Can I run it? for this device.
How to run Ternary Bonsai 2 27B on a Mac with 16 GB
- Easiest: LM Studio or Ollama. Both run on Apple Silicon GPUs. LM Studio also runs MLX models, usually the fastest on a Mac.
Every model page lists the same commands for its own files. Ternary Bonsai 2 27B's page also has a memory table for other context lengths.
Questions
What is the best LLM for a Mac with 16 GB?
As of November 2026, the best model to run on a Mac with 16 GB is Ternary Bonsai 2 27B by PrismML (Small LM Index 71.2), at about 10 tokens/s with Q2_0 (as released) weights. For faster replies, LFM3 1.7B (61.2) runs at about 42 tokens/s.
What is the biggest model that runs on a Mac with 16 GB?
Ternary Bonsai 2 27B (27.4B parameters) fits at Q2_0 (as released), needing 9.3 GB and generating about 10 tokens/s. Bigger models may fit at lower quality or run more slowly; try them in the Can I run it? tool.
How much memory can a model use on a Mac with 16 GB?
About 11 GB of the 16 GB. A MacBook Air or Mac mini with 16 GB shares its memory between the system and the GPU; macOS lets the GPU use about two thirds of it, roughly 10–11 GB.
How accurate are these speeds?
They are estimates from memory bandwidth (120 GB/s here), which limits how fast a model generates text, with a typical efficiency for this kind of hardware. Expect real speeds within about a third either way. Long prompts take longer to read before the first word appears.