SpinQuant introduces a novel approach to post-training quantization for Large Language Models that significantly reduces memory usage, latency, and power consumption while preserving performance.

• Tackles the critical outlier problem in quantization through intelligent rotation of activation/weight matrices
• Identifies optimal rotation parameterizations that maintain full-precision outputs
• Enables more efficient deployment of LLMs across resource-constrained environments
• Supports practical engineering applications by balancing model performance with computational efficiency

This research addresses a key engineering challenge in LLM deployment, making advanced AI capabilities more accessible and sustainable for real-world applications without quality degradation.

SpinQuant: LLM quantization with learned rotations