Vision Transformers (ViTs) achieve strong performance across vision tasks, yet their deployment with low-precision early exiting remains fragile. Existing quantization methods assume static full-depth execution, making them unstable when exit decisions are perturbed by quantization noise, which can amplify errors along dynamic inference paths. In this paper, we introduce Amortized-Precision Quantization (APQ), a utilization-aware formulation that accounts for layer utilization when modeling quantization error and reveals depth-precision trade-offs. Building on APQ, we propose Mutual Adaptive Quantization with Early Exiting (MAQEE), a bi-level framework that jointly optimizes exit thresholds and bit-widths under explicit risk control to improve inference stability. MAQEE establishes a superior Pareto frontier in the accuracy-efficiency trade-off across classification, detection, and segmentation, with comparisons at predefined task-specific performance targets. On ImageNet, it achieves 79.41% Top-1 accuracy while reducing BOPs by 10.9% relative to utilization-only allocation. Measurements of the deployed models on an RTX 5090 show GPU throughput improvements of $2.26\times$ on ImageNet and $3.06\times$ on CIFAR-100 over full-depth FP32 inference. Source code is available at https://github.com/Rui-Fang/MAQEE.

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