DP-LLM: Runtime Model Adaptation with Dynamic Layer-wise Precision Assignment
Sangwoo Kwon, Seong Hoon Seo, Jae W. Lee, Yeonhong Park
Abstract
How can we effectively handle queries for on-device large language models (LLMs) with varying runtime constraints, such as latency and accuracy? Multi-scale quantization addresses this challenge by enabling memory-efficient runtime model adaptation of LLMs through the overlaying of multiple model variants quantized to different bitwidths. Meanwhile, an important question still remains open-ended: how can models be properly configured to match a target precision or latency? While mixed-precision offers a promising solution, we take this further by leveraging the key observation that the sensitivity of each layer dynamically changes across decoding steps. Building on this insight, we introduce DP-LLM, a novel mechanism that dynamically assigns precision to each layer based on input values. Experimental results across multiple models and benchmarks demonstrate that DP-LLM achieves a superior performance-latency trade-off, outperforming prior approaches.
BibTeX
@inproceedings{
kwon2025dpllm,
title={{DP}-{LLM}: Runtime Model Adaptation with Dynamic Layer-wise Precision Assignment},
author={Sangwoo Kwon and Seong Hoon Seo and Jae W. Lee and Yeonhong Park},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=ppKDXf55lY}
}