Researchers from ETH Zurich, EPFL, and Microsoft Introduce QuaRot: A Machine Learning Method that Enables 4-bit Inference of LLMs by Removing the Outlier Features

 Researchers from ETH Zurich, EPFL, and Microsoft Introduce QuaRot: A Machine Learning Method that Enables 4-bit Inference of LLMs by Removing the Outlier Features

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QuaRot: A Breakthrough in Large Language Model Optimization

Large language models (LLMs) have transformed various industries with their advanced natural language processing capabilities. However, their significant computational and memory demands hinder their deployment and operational efficiency. Researchers have turned to quantization to reduce these demands, but outliers within the data present a persistent challenge.

Introducing QuaRot

QuaRot is a breakthrough approach by researchers from ETH Zurich, EPFL, Microsoft Research, IST Austria, and NeuralMagic. It offers a promising solution by applying a novel quantization scheme based on rotations to mitigate the effects of outliers. This method allows for a comprehensive 4-bit quantization encompassing all model components, significantly diminishing the model’s computational and memory requirements.

Performance and Impact

The efficacy of QuaRot is underscored by its performance on the LLAMA 2-70B model, achieving remarkable outcomes and enabling up to 2.16 times speedup during the prefill phase of inference and substantial reduction in memory usage. These improvements reduce operational costs and energy consumption associated with running such advanced models.

Broader Adoption and Deployment

By enabling end-to-end 4-bit inference without significant performance loss, QuaRot allows for the broader adoption and deployment of LLMs across various devices, driving innovation and expanding their applicability in sectors with limited computational resources.

Conclusion

QuaRot marks a significant leap forward in optimizing large language models, successfully addressing the challenge of efficiently quantizing LLMs while maintaining high accuracy. The method’s ability to reduce memory usage and computational demands is evidenced by its LLAMA 2-70B model performance.

Check out the Paper and Github.

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If you want to evolve your company with AI, stay competitive, and use AI to your advantage, consider leveraging QuaRot to enable 4-bit inference of LLMs. Discover how AI can redefine your way of work and identify automation opportunities, define KPIs, select an AI solution, and implement gradually.

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