Abstract: Modern GPUs offer an accessible and scalable supercomputing platform. However, their use for state-space search has so far been auxiliary or in niche methods such as iterative deepening A* or random walks. We describe a scalable deterministic A* state space search implementation running entirely on the GPU. This is made possible by designing core algorithms for lock-step parallelism as well as using CUDA cooperative grids to implement hash-distributed A* load balancing scheme. Comparative benchmarking showed significant improvements in performance as well as resource efficiency.