Research

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

SLaB's sparse-lowrank-binary weight decomposition reduces LLM inference costs and memory overhead through structured factorization.

Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline

SLaB introduces a sparse-lowrank-binary decomposition technique for efficient large language models. The method factorizes model weights to reduce computational and memory overhead while preserving capabilities. This targets practical deployment and cost reduction for large model inference.

Read original at arXiv CS.LG (Machine Learning)

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