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Traditional quantization methods, such as , often struggle with "outlier" weights—individual parameters that have a disproportionate impact on the model's output. When these outliers are forced into low-bit representations (like 4-bit), the model's perplexity (accuracy) degrades significantly. 2. Technical Mechanism

SpQR represents a shift from uniform quantization to . By treating weights differently based on their importance, it bridges the gap between massive model scales and accessible hardware.

: It uses a Hessian-based regularizer to identify which weights are most sensitive to quantization.

: Optimization for specific GPU architectures (e.g., NVIDIA Ampere or Hopper). Conclusion

: Pre-defined sparsity levels (e.g., 1% outliers) to ensure predictable memory usage.

The SpQR framework, as detailed in the ICLR Proceedings , operates through a multi-step process: