DP-LLM: Runtime Model Adaptation with Dynamic Layer-wise Precision Assignment

Sangwoo Kwon (Seoul National University) · Jae W. Lee (Seoul National University) · Seong Hoon Seo (Seoul National University) · Yeonhong Park (Seoul National University)

adaptive quantizationbitwidthsdecoding stepsdynamic precision assignmentexperimental benchmarkslayer-wise optimizationmemory-efficient runtime adaptationmixed-precisionmodel configurationmodel variantsmulti-scale quantizationon-device large language modelsperformance-latency trade-offsensitivity analysistarget precision

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.