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Understanding LLM Quantization through Activation-Guided Compensation and Orthogonal Residuals
Post-training weight-activation quantization reduces the memory and inference costs of large language models, but aggressive W4A4 quantization remains difficult because activation outliers degrade effective quantization resolution. Although weight optimization, channel-wise scaling, and orthogonal r
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