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We present GRaM-X (General Relativistic accelerated Magnetohydrodynamics onAMReX), a new GPU-accelerated dynamical-spacetime general relativisticmagnetohydrodynamics (GRMHD) code which extends the GRMHD capability ofEinstein Toolkit to GPU-based exascale systems. GRaM-X supports 3D adaptivemesh refinement (AMR) on GPUs via a new AMR driver for the Einstein Toolkitcalled CarpetX which in turn leverages AMReX, an AMR library developed for useby the United States DOE's Exascale Computing Project (ECP). We use the Z4cformalism to evolve the equations of GR and the Valencia formulation to evolvethe equations of GRMHD. GRaM-X supports both analytic as well as tabulatedequations of state. We implement TVD and WENO reconstruction methods as well asthe HLLE Riemann solver. We test the accuracy of the code using a range oftests on static spacetime, e.g. 1D MHD shocktubes, the 2D magnetic rotor and acylindrical explosion, as well as on dynamical spacetimes, i.e. theoscillations of a 3D TOV star. We find excellent agreement with analyticresults and results of other codes reported in literature. We also performscaling tests and find that GRaM-X shows a weak scaling efficiency of $\sim40-50\%$ on 2304 nodes (13824 NVIDIA V100 GPUs) with respect to single-nodeperformance on OLCF's supercomputer Summit.

GRaM-X: A new GPU-accelerated dynamical spacetime GRMHD code for Exascale computing with the Einstein Toolkit