Learning-based 3D reconstruction models, represented by Visual Geometry Grounded Transformers (VGGTs), have achieved remarkable progress with large-scale transformers. Their prohibitive computational and memory costs severely hinder real-world deployment. Post-Training Quantization (PTQ) has emerged as a common practice to compress and accelerate models. However, we empirically observe that PTQ faces unique obstacles when compressing billion-scale VGGTs: the data-independent special tokens induce heavy-tailed activation distributions, while the multi-view nature of 3D data makes calibration sample selection highly unstable. This paper proposes the first **Quant**ization framework for **VGGT**s, namely **QuantVGGT**. This mainly relies on two technical contributions: First, we introduce *Dual-Smoothed Fine-Grained Quantization*, which integrates pre-global Hadamard rotation and post-local channel smoothing to robustly mitigate heavy-tailed distributions and inter-channel variance. Second, we design *Noise-Filtered Diverse Sampling*, which filters outliers via deep-layer statistics and constructs frame-aware diverse calibration clusters to ensure stable quantization ranges. Comprehensive experiments demonstrate that QuantVGGT achieves the state-of-the-art results across different benchmarks and bit-width, surpassing the previous state-of-the-art generic quantization method with a great margin. We highlight that our 4-bit QuantVGGT can deliver a **3.7$\times$** memory reduction and **2.5$\times$** acceleration in real-hardware inference, while preserving over **98\%** reconstruction accuracy of the full-precision counterparts. This demonstrates the vast advantages and practicality of QuantVGGT in resource-constrained scenarios.
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