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tech 4 August 2026

Run an 80B Qwen Model with 4.3 GB of RAM on Mac and a 35B on iPhone

Learn how to run an 80 billion parameter Qwen model on your Mac with just 4.3 GB of RAM, and a 35 billion parameter model on an iPhone.

Article inspired by the original source
Show HN: Run an 80B Qwen in 4.3 GB of RAM on a Mac, and a 35B on an iPhone ↗ github.com

Introduction

In the realm of artificial intelligence, resource optimization is crucial. With the exponential growth in model sizes, it becomes imperative to find efficient ways to run them on consumer-grade hardware. This is where Swiftlet comes into play, an innovative solution that enables large models to run with reduced memory consumption.

What is Swiftlet?

Swiftlet is an open-source project aimed at making massive AI models accessible on devices with limited resources. It uses advanced compression and optimization techniques to run models like the 80-billion parameter Qwen on a Mac with just 4.3 GB of available RAM.

Technology Behind Swiftlet

The magic of Swiftlet is based on several key technologies:

  1. Quantization: Reducing the precision of model weights to decrease memory usage without sacrificing performance.
  2. Pruning: Removing redundant connections in the neural network to reduce model size.
  3. Graph Optimization: Reorganizing computations to maximize efficiency.

Concrete Example

Consider running an 80B Qwen model on a MacBook Pro with 4.3 GB of RAM. Thanks to Swiftlet, what seemed impossible is now achievable. By using quantization and pruning techniques, the model is significantly reduced without notable loss in accuracy.

Application on iPhone

Swiftlet isn’t limited to laptops. With iPhones, which have even more restricted resources, Swiftlet enables the execution of 35 billion parameter models. This opens up possibilities for developing mobile applications based on AI that require advanced processing capabilities.

Implications for Developers

For developers, Swiftlet is a boon. It allows:

  • Accessibility: Running complex models on widely available hardware.
  • Cost Savings: Reducing hardware needs and thus associated costs.
  • Innovation: Opening new possibilities for mobile and desktop applications.

Conclusion

Swiftlet is a game-changer in AI model deployment. Whether you’re a developer, entrepreneur, or tech decision-maker, this technology will allow you to push the boundaries of what is possible with available resources.

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Swiftlet AI models resource optimization quantization pruning
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