SLaB: Sparse-Lowrank-Binary Decomposition for Efficient Large Language Models

📰 ArXiv cs.AI

SLaB decomposes large language model weights into sparse, low-rank, and binary components for efficient deployment

advanced Published 7 Apr 2026

Action Steps

Decompose each linear layer weight into sparse, low-rank, and binary components
Apply sparse decomposition to reduce memory usage
Use low-rank decomposition to decrease computational complexity
Combine binary decomposition with sparse and low-rank components for efficient deployment

Who Needs to Know This

ML researchers and engineers on a team can benefit from SLaB to improve the efficiency of large language models, while maintaining good performance

Key Insight

💡 Decomposing large language model weights into complementary components can lead to efficient deployment without sacrificing performance

Key Takeaways

SLaB decomposes large language model weights into sparse, low-rank, and binary components for efficient deployment

Full Article

Title: SLaB: Sparse-Lowrank-Binary Decomposition for Efficient Large Language Models

Abstract:
arXiv:2604.04493v1 Announce Type: cross Abstract: The rapid growth of large language models (LLMs) presents significant deployment challenges due to their massive computational and memory demands. While model compression, such as network pruning, offers potential solutions, most existing methods often fail to maintain good performance at high compression ratios. To address this, we propose SLaB, a novel framework that decomposes each linear layer weight into three complementary components: a spa

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