ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation

ReSpinQuant introduces an efficient quantization framework for large language models that combines the expressivity of layer-wise adaptation with the computational efficiency of global rotation methods. By leveraging offline activation rotation fusion and residual subspace rotation matching, the approach achieves state-of-the-art performance on aggressive quantization schemes (W4A4, W3A3) without significant inference overhead.

ReSpinQuant addresses a fundamental engineering challenge in LLM deployment: reducing model size through quantization while maintaining accuracy and inference speed. The problem stems from competing design philosophies in existing quantization approaches. Global rotation methods efficiently fuse computations into model weights, enabling fast inference, but their single learnable rotation matrix across all layers lacks the flexibility to handle layer-specific activation distributions. Layer-wise methods overcome this expressivity limitation through localized adaptation but require online rotation computations during inference, creating substantial computational overhead that undermines their practical utility.

This research represents incremental but meaningful progress in the quantization space, which has become increasingly critical as organizations seek to deploy large language models cost-effectively. The field has matured significantly from simple post-training quantization toward sophisticated techniques that account for activation outliers—a known bottleneck in aggressive quantization schemes. The ability to achieve W3A3 quantization (3-bit weights and activations) approaches the theoretical limits of practical quantization while maintaining usable accuracy.

For practitioners and infrastructure providers, ReSpinQuant offers a pathway to reduced computational costs during model inference without sacrificing model quality. This matters particularly for organizations running inference at scale, where eliminating online rotation overhead translates directly to faster response times and lower energy consumption. The technique demonstrates that efficient solutions don't require choosing between expressivity and speed—careful mathematical optimization can reconcile both objectives.

The practical impact depends on whether these results generalize beyond benchmark tasks and whether the approach integrates smoothly into existing deployment pipelines. Future work should validate performance on diverse model architectures and real-world inference workloads.

• →ReSpinQuant eliminates inference overhead of layer-wise quantization methods through offline activation rotation fusion
• →Achieves state-of-the-art W4A4 and W3A3 quantization by combining global efficiency with local adaptation expressivity
• →Residual subspace rotation matching enables effective weight-rotation fusion while maintaining accuracy
• →Method bridges the efficiency-expressivity gap that has plagued competing quantization approaches
• →Results suggest aggressive quantization of large language models is increasingly practical for deployment at scale