Developed a major feature for the JuliaGPU/CUDA.jl repository, enabling batched eigenvalue computations on 3D StridedCuArray inputs through the new XsyevBatched! interface. This work leveraged Julia and GPU programming techniques to support efficient linear algebra operations on large-scale 3D matrices. The implementation included cuSOLVER version compatibility checks to ensure robust performance across diverse GPU environments, as well as memory-aware buffer size calculations to optimize resource usage and minimize allocation overhead. The feature addressed the need for scalable, high-performance numerical computing workflows, demonstrating depth in both GPU programming and linear algebra within the Julia ecosystem for scientific computing.