Efficient Large Language Models with Zero-Shot Adjustable Acceleration

• URL: http://arxiv.org/abs/2509.01190v2
• Date: Sat, 06 Sep 2025 17:46:00 GMT
• Title: Efficient Large Language Models with Zero-Shot Adjustable Acceleration
• Authors: Sajjad Kachuee, Mohammad Sharifkhani,
• Abstract summary: This paper introduces Zero-Shot Adjustable Acceleration, a novel training and inference method that adjusts dynamically hardware utilization during inference without requiring additional fine-tuning.<n> Experimental results demonstrate that the method supports a wide range of zero-shot acceleration and achieves up to 11x speedup compared to the baseline.
• Score: 4.125187280299246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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