The direct eigensolver library ELPA has long offered unrivalled performance for parallel matrix diagonalizations. Extensive profiling, fine tuning and work on portability was carried out to adapt ELPA to the most current HPC architectures, further reducing the time for the diagonalization bottleneck for simulation sizes up to many thousands of atoms. Key to future success of ELPA is exploiting full capabilities of GPU-accelerated high-performance clusters. ELPA already has a well-established support for NVIDIA GPUs . Recently, we ported ELPA to AMD GPUs, enabling the solution of a problem with a matrix size with leading dimension of more than 3 million on 1024 AMD-GPU nodes of the LUMI pre-exascale system at CSC in Finland.