Layer-wise dynamic rank for compressing large language models

• URL: http://arxiv.org/abs/2509.25622v2
• Date: Sat, 04 Oct 2025 02:57:59 GMT
• Title: Layer-wise dynamic rank for compressing large language models
• Authors: Zhendong Mi, Bian Sun, Grace Li Zhang, Shaoyi Huang,
• Abstract summary: Large language models (LLMs) have rapidly scaled in size, bringing severe memory and computational challenges.<n>We propose D-Rank, a framework with layer-wise balanced Dynamic Rank allocation for LLMs compression.
• Score: 2.9416461160070955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have rapidly scaled in size, bringing severe memory and computational challenges that hinder their deployment. Singular Value Decomposition (SVD)-based compression has emerged as an appealing post-training compression technique for LLMs, yet most existing methods apply a uniform compression ratio across all layers, implicitly assuming homogeneous information included in various layers. This overlooks the substantial intra-layer heterogeneity observed in LLMs, where middle layers tend to encode richer information while early and late layers are more redundant. In this work, we revisit the existing SVD-based compression method and propose D-Rank, a framework with layer-wise balanced Dynamic Rank allocation for LLMs compression. We first introduce effective rank as a principled metric to measure the information density of weight matrices, and then allocate ranks via a Lagrange multiplier-based optimization scheme to adaptively assign more capacity to groups with higher information density under a fixed compression ratio. Moreover, we rebalance the allocated ranks across attention layers to account for their varying importance and extend D-Rank to latest LLMs with grouped-query attention. Extensive experiments on various LLMs with different scales across multiple compression ratios demonstrate that D-Rank consistently outperforms SVD-LLM, ASVD, and Basis Sharing, achieving more than 15 lower perplexity with LLaMA-3-8B model on C4 datasets at 20% compression ratio and up to 5% higher zero-shot reasoning accuracy with LLaMA-7B model at 40% compression ratio while achieving even higher throughput.

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