Accelerating Large Language Model Inference via Early-Exiting Algorithms

• URL: http://arxiv.org/abs/2509.05915v1
• Date: Sun, 07 Sep 2025 04:20:14 GMT
• Title: Accelerating Large Language Model Inference via Early-Exiting Algorithms
• Authors: Sangmin Bae,
• Abstract summary: dissertation: Co-designing adaptive algorithms and model architectures to strike an optimal balance between dynamism and efficiency.<n>We first address critical sources of overhead in conventional early-exiting by proposing an efficient parallel decoding mechanism.<n>We then show that deep parameter sharing provides an architectural foundation that not only yields compact, parameter-efficient models but also inherently mitigates the critical synchronization issues affecting dynamic inference.
• Score: 10.338409447316373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models have achieved remarkable capabilities, but their practical deployment is hindered by significant computational costs. While adaptive computation methods like early-exiting promise to reduce these costs, they introduce a fundamental conflict: the per-token dynamism intended to save computation often creates system-level bottlenecks that can paradoxically reduce throughput in batched inference. This dissertation resolves this conflict by co-designing adaptive algorithms and model architectures to strike an optimal balance between dynamism and efficiency. To this end, our work first addresses critical sources of overhead in conventional early-exiting by proposing an efficient parallel decoding mechanism. We then show that deep parameter sharing provides an architectural foundation that not only yields compact, parameter-efficient models but also inherently mitigates the critical synchronization issues affecting dynamic inference. Finally, this work presents a unified framework where lightweight routers are pretrained to dynamically assign an optimal recursion depth for each token. This approach establishes a new Pareto frontier between efficiency and performance by effectively optimizing for both adaptive computation and parameter efficiency within a single model.

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