ClusComp introduces a novel compression paradigm that clusters weight matrices into codebooks and finetunes them block-by-block, addressing key challenges in deploying large language models.

• Achieves superior performance compared to standard quantization methods, especially at lower bit widths
• Enables efficient finetuning of compressed models without typical performance degradation
• Supports edge deployment by significantly reducing model size while maintaining capabilities
• Offers a simple yet effective approach that works across different model architectures

This innovation matters for engineering teams seeking to deploy powerful AI models on resource-constrained devices or reduce infrastructure costs while maintaining model performance.

ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning