LLM Compression with Convex Optimization—Part 1: Weight Quantization

ICLR 2025 Conference Submission954 Authors

Keywords: weight quantization, model compression, large language models

TL;DR: For large language model compression, weight quantization using convex optimization leads to superior compressed model performance

Abstract: In recent years, compression of large language models (LLMs) has emerged as an important problem to enable language model deployment on resource-constrained devices, reduce computational costs, and mitigate the environmental footprint of large-scale AI infrastructure. In this paper, we lay down the foundation for LLM quantization from a convex optimization perspective and propose a quantization technique that builds on this foundation for optimum quantization outcomes. Our quantization framework, CVXQ, scales to models containing hundreds of billions of weight parameters and provides users with the flexibility to compress models to any specified model size, post-training. A reference implementation of CVXQ can be obtained from.

Primary Area: optimization

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Submission Number: 954