Low-bit quantization is crucial for deploying Large Language Models (LLMs) on resource-constrained hardware. However, existing Post-Training Quantization (PTQ) methods adopt a monolithic view of outliers, overlooking their global spatial patterns within LLM architectures and weights that are quantization-sensitive but not numerically large. To overcome these challenges, we introduce MOAQ, a novel finetuning-free weight-only PTQ framework based on a new perspective for outlier analysis. MOAQ holistically addresses the outlier problem by three core components: (1) Spatial Calibration (SC) uses a clustering-optimized Hadamard matrix to reorganize disordered outliers into both discrete and clustered patterns. (2) Local Awareness and Suppression (LAS) adopts a dual strategy guided by our outlier metric. It assigns higher bit-widths to clustered outliers via Clustered Outlier Bit Search (COBS), enabling flexible fractional-bit quantization, while further refining quantization parameters for local discrete outliers through Local Outlierness-Aware Calibration (LOAC). (3) Global Feedback and Denoising (GFD) introduces a dynamic correction mechanism informed by our outlier metric to mitigate the progressive cross-layer enrichment of outliers. Extensive experiments show that MOAQ achieves state-of-the-art performance, outperforming both finetuning-free and many finetuning-based methods with substantially lower computational cost.