We present an approach to enhance storage efficiency and reduce memory bandwidth utilization in modal Discontinuous Galerkin (DG) methods by introducing a customized mixed-precisionrepresentation for the solution vector. Our approach leverages variations in floating-point accuracy requirements among the local degrees of freedom associated with different modal basis functions. Using a common exponent, we represent the local solution vector compactly, optimizing storage efficiency. This approach significantly reduces memory usage while preserving numerical accuracy. To fully utilize this new representation, we design specialized arithmetic operations for the new datatype —addition, subtraction, multiplication, and division— ensuring stability and precision.The findings highlight the potential of mixed precision to balance accuracy and performance, enabling scalable and efficient implementations of DG methods on modern HPC architectures. This study provides practical insights and guidelines for integrating mixed-precision strategies into high-order numerical methods, promoting broader adoption in computational science.