Run Qwen 2.5 Coder 14B on iPad & Mac
Qwen 2.5 Coder 14B runs 100% private on iPad & 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 Qwen 2.5 Coder 14B, 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
- 14.8B
- Context window
- 33K tokens
- License
- Apache 2.0
- Quantization
- GPTQ-Int4
- Family
- Qwen 2.5 14B
What Qwen 2.5 Coder 14B is good at
Qwen2.5 Coder 14B Instruct is a large language model specialized for coding tasks. It is good at code generation, code reasoning, and code fixing. The model also supports long contexts up to 128,000 tokens.
Which of your devices can run it
iPad
Mac
How to run Qwen 2.5 Coder 14B in Private LLM
- Download Private LLM from the App Store.
- Open the in-app model library and choose Qwen 2.5 Coder 14B.
- Download the model once, then chat fully offline.
Variants & related models
Frequently asked questions
Qwen 2.5 Coder 14B is too large for current iPhones. It runs on Mac with enough unified memory inside Private LLM, fully offline.
Yes. Qwen 2.5 Coder 14B 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, Qwen 2.5 Coder 14B 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 Qwen 2.5 Coder 14B 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 Qwen 2.5 Coder 14B 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 Qwen 2.5 Coder 14B's Hugging Face model card, released under the Apache 2.0 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.