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arXiv · 2601.21623

LAMP: Look-Ahead Mixed-Precision Inference of Large Language Models

Stanislav Budzinskiy·Marian Gloser·Tolunay Yilmaz·Ying Hong Tham·Yuanyi Lin·Wenyi Fang·Fan Wu·Philipp Petersen
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Abstract

Mixed-precision computations are a hallmark of the current stage of AI, driving the progress in large language models towards efficient, locally deployable solutions. This article addresses the floating-point computation of compositionally-rich functions, concentrating on transformer inference. Based on the rounding error analysis of a composition $f(g(\mathrm{x}))$, we provide an adaptive strategy that selects a small subset of components of $g(\mathrm{x})$ to be computed more accurately while all other computations can be carried out with lower accuracy. We then explain how this strategy can be applied to different compositions within a transformer and illustrate its overall effect on transformer inference. We study the effectiveness of this algorithm numerically on GPT-2 models and demonstrate that already very low recomputation rates allow for improvements of up to two orders of magnitude in accuracy.

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Stanislav Budzinskiy, Marian Gloser, Tolunay Yilmaz, Ying Hong Tham, Yuanyi Lin, Wenyi Fang, Fan Wu, Philipp Petersen. 2026-01-29. LAMP: Look-Ahead Mixed-Precision Inference of Large Language Models. https://arxiv.org/abs/2601.21623

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