[Models](/models) / [Qwen 2.5 32B](/models?family=Qwen%202.5%2032B#models-gallery) / Qwen 2.5 32B

![Qwen logo](/model-logos/qwen.svg)

# Run Qwen 2.5 32B on Mac

Qwen 2.5 32B runs 100% private on Mac inside Private LLM — no internet connection required, no data sent to any server.

32.8BMac

## Download the quantized weights

These are the exact GPTQ-Int4 weights Private LLM runs for Qwen 2.5 32B, published on our Hugging Face org. They're standard weights you can load in any app that supports the format, not just Private LLM.

-   [Mac — GPTQ-Int4 →](https://huggingface.co/numen-tech/Qwen2.5-32B-Instruct-GPTQ-Int4)

## Specifications

Parameters

32.8B

Context window

33K tokens

License

Apache 2.0

Quantization

GPTQ-Int4

Family

Qwen 2.5 32B

## What Qwen 2.5 32B is good at

general

Qwen2.5 32B Instruct is a large language model with 32.5 billion parameters, designed to follow instructions and assist with tasks. It is good at coding, mathematics, following complex directions, and generating long, structured outputs like JSON. The model supports over 29 languages and can handle very long contexts up to 128,000 tokens.

## Which of your devices can run it

### Mac

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)

Browse every model that fits [iPhone](/models/best-for-iphone) or [Mac](/models/best-for-mac).

## How to run Qwen 2.5 32B in Private LLM

1.  Download Private LLM from the App Store.
2.  Open the in-app model library and choose Qwen 2.5 32B.
3.  Download the model once, then chat fully offline.

[![Download Private LLM on the App Store](/app-store/download-badge/en/download.svg)![Download Private LLM on the App Store](/app-store/download-badge/en/download-dark.svg)](/download)[Join our Discord](/discord)

## Variants & related models

![Qwen logo](/model-logos/qwen.svg)

### EVA Qwen2.5 32B v0.2

32.8B

creative

131K contextMac

[View details →](/models/eva-qwen2.5-32b-v0.2)

![Qwen logo](/model-logos/qwen.svg)

### OpenHands LM 32B v0.1

32.8B

coding

33K contextMac

[View details →](/models/openhands-lm-32b-v0.1)

![Qwen logo](/model-logos/qwen.svg)

### Qwen 2.5 Coder 32B

32.8B

coding

33K contextMac

[View details →](/models/qwen2.5-coder-32b-instruct)

![Meta logo](/model-logos/meta.svg)

### Airoboros l2 7b 3.0

6.7B

general

4K contextiPhone & iPad · Mac

[View details →](/models/airoboros-l2-7b-3.0)

![Mistral logo](/model-logos/mistral.svg)

### Airoboros M 7B

7.2B

general

33K contextMac

[View details →](/models/airoboros-m-7b-3.1.2)

![Meta logo](/model-logos/meta.svg)

### Cat Llama 3 70B Instruct

70.6B

general

8K contextMac

[View details →](/models/cat-llama-3-70b-instruct)

## Frequently asked questions

-   Can I run Qwen 2.5 32B on iPhone?
    
    Qwen 2.5 32B is too large for current iPhones. It runs on Mac with enough unified memory inside Private LLM, fully offline.
    
-   Can I run Qwen 2.5 32B on Mac?
    
    Yes. Qwen 2.5 32B 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), fully on-device in Private LLM.
    
-   Does Qwen 2.5 32B work offline?
    
    Yes. Once downloaded in Private LLM, Qwen 2.5 32B runs 100% on-device — no internet connection, and nothing is sent to any server.
    
-   Is Qwen 2.5 32B free to use?
    
    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 32B 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 32B 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](/en/faq#Why-can-t-Private-LLM-load-models-directly-from-Hugging-Face).

### More from Numen

[![Slop or Not app icon](/app-icons/slop-or-not.png)

#### Slop or Not

Catch AI-written text on iPhone and Mac. The check runs on-device, so nothing you paste gets uploaded.

](https://slopornot.ai)[![Clean Links app icon](/app-icons/clean-links.png)

#### Clean Links

Strip trackers and clutter from any link before you share it. The cleanup happens on your device.

](https://cleanlinks.app)

Specifications and summary come from Qwen 2.5 32B's [Hugging Face model card](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct), 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.