Efficient Large Language Models with Zero-Shot Adjustable Acceleration

Sajjad Kachuee; Mohammad Sharifkhani

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arXiv · 2509.01190

Efficient Large Language Models with Zero-Shot Adjustable Acceleration

Sajjad Kachuee·Mohammad Sharifkhani

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Abstract

Using Large Language Models (LLMs) in real-world applications presents significant challenges, particularly in balancing computational efficiency with model performance. Optimizing acceleration after fine-tuning and during inference is critical for building efficient architectures. This paper introduces Zero-Shot Adjustable Acceleration, a novel training and inference method that dynamically adjusts hardware utilization during inference without requiring additional fine-tuning. The proposed approach is applied to recent LLMs and evaluated across multiple classification and text generation tasks. Experimental results demonstrate that the method supports a wide range of zero-shot acceleration and achieves up to 11x speedup compared to the baseline.

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Sajjad Kachuee, Mohammad Sharifkhani. 2025-09-01. Efficient Large Language Models with Zero-Shot Adjustable Acceleration. https://arxiv.org/abs/2509.01190

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