Run Phi 4 on Mac
Phi 4 runs 100% private on Mac inside Private LLM — no internet connection required, no data sent to any server.
Download the quantized weights
These are the exact GPTQ-Int4 weights Private LLM runs for Phi 4, published on our Hugging Face org. They're standard weights you can load in any app that supports the format, not just Private LLM.
Specifications
- Parameters
- 14.7B
- Context window
- 16K tokens
- License
- MIT
- Quantization
- GPTQ-Int4
- Family
- Phi 4 14B
What Phi 4 is good at
Phi 4 is a 14 billion parameter language model from Microsoft Research, trained on high quality text to handle reasoning and logic tasks. It is designed for use in environments with limited memory or computing power and where fast responses are needed. The model performs well on benchmarks for math, science, and code generation compared to similar sized models.
Which of your devices can run it
Mac
How to run Phi 4 in Private LLM
- Download Private LLM from the App Store.
- Open the in-app model library and choose Phi 4.
- Download the model once, then chat fully offline.
Variants & related models
Frequently asked questions
Phi 4 is too large for current iPhones. It runs on Mac with enough unified memory inside Private LLM, fully offline.
Yes. Phi 4 runs on Macs with enough unified memory, such as Mac (Apple Silicon, 192GB), MacBook Pro (M4 Max, 128GB), Mac Studio / Pro (Apple Silicon, 96GB), MacBook Pro (M4 Max, 64GB), MacBook Pro (M4 Max, 48GB), MacBook Pro (M4 Max, 36GB), Mac (Apple Silicon, 32GB), MacBook Air (M4, 24GB), MacBook Air (M-series, 16GB), fully on-device in Private LLM.
Yes. Once downloaded in Private LLM, Phi 4 runs 100% on-device — no internet connection, and nothing is sent to any server.
Private LLM is a one-time purchase with no subscription and no per-message cost. The models themselves are open source — once downloaded, they run offline with nothing to pay per use.
Why run Phi 4 in Private LLM
Private LLM has run local AI on iPhone, iPad, and Mac since 2023, before Apple Intelligence existed. Inference happens on your device, so your Phi 4 conversations never reach a server. The part most apps gloss over is quantization, and that is exactly where on-device quality is won or lost. Most llama.cpp and MLX wrappers ship the same off-the-shelf 4-bit RTN weights. Private LLM ships GPTQ and OmniQuant quantization, tuned per model, and our 3-bit OmniQuant models match or beat those 4-bit RTN builds on the same Apple Silicon. Run the same model both ways and you feel it in the first reply. See how our quantization works.
Specifications and summary come from Phi 4's Hugging Face model card, released under the MIT license. Private LLM ships its own quantized models, built with GPTQ-Int4 quantization tuned per model, and isn't affiliated with the model's authors.