Learning-based 3D deformable registration is central to many clinical workflows for a plethora of applications. However, state-of-the-art models are memory and compute intensive, which limits deployment on local hospital workstations and other resource-constrained environments where larger memory footprint translates to slower inference. While post-training quantization has been widely explored in image classification and segmentation, it has not been systematically studied for medical image registration. We present, to our knowledge, the first systematic evaluation of low bit weight quantization for modern 3D registration across four architectures (VoxelMorph, NICE-Net, TransMorph, NICE-Trans) and three CT benchmarks (CrossTemporal, NLST, UCLA5DCT), comparing our novel mixed precision angle based quantizaton against the standard MSE based approaches. Our main contribution is an angle-aware mixed-precision scheme that assigns non-uniform bit-widths to convolutional layers using a novel importance score that combines network topology with the angular deviation between full-precision and quantized weight vectors. Inspired by our correlation study that shows that angle error correlates more strongly with registration loss than conventional weight MSE, establishing angular distortion as a more faithful proxy for performance degradation and the central signal driving our bit-assignment policy. Across all models and datasets, the proposed angle-aware mixed-precision strategy matches or improves target registration error and multi-organ Dice relative to RTN and DFQ at comparable or lower average bit-widths, while achieving substantial reductions in model size compared with FP32. These results indicate that carefully designed angle-aware quantization enables aggressive compression of 3D registration networks with minimal performance loss, making high-quality registration more practical in resource-limited clinical settings.