In this paper, we propose a novel quantization technique for Bayesian deep learning aimed at enhancing efficiency without compromising performance. Our approach leverages post-training quantization to significantly reduce the memory footprint of stochastic gradient samplers, particularly Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods. This technique achieves a level of compression comparable to optimal thinning, which traditionally necessitates not only the original samples in single precision floating-point representation but also the gradients, resulting in substantial computational overhead. In contrast, our quantization method requires only the original samples and can accurately recover posterior modes through a simple affine transformation. This process incurs minimal additional memory or computational costs, making it a highly efficient alternative for Bayesian deep learning applications.