This study introduces NEMESYS, a novel algorithm designed to exploit the parallel processing capabilities of Graphics Processing Units (GPUs) to significantly enhance computational efficiency in fluid dynamics simulations. NEMESYS integrates the Reynolds-Averaged Navier-Stokes (RANS) equations with the k−ε turbulence model, and its efficacy is validated through simulations of two classical flow scenarios: laminar flow around a cylinder, exhibiting von Kármán vortex shedding, and turbulent flow over a backward-facing step. The algorithm's performance is critically assessed against established benchmarks from scientific literature and leading commercial Computational Fluid Dynamics (CFD) software. Key performance metrics include the Strouhal number for the cylinder flow and reattachment length for the backward-facing step flow. Results demonstrate remarkable accuracy and reliability of NEMESYS, with a notable reduction in computation time − up to 99.5% faster than traditional CPU-based software. This substantial reduction in computational effort presents significant cost savings and opens new avenues for real-time analysis and accelerated design processes. The implications of this advancement are far-reaching, offering transformative potential for various engineering domains, such as automotive, civil, and environmental engineering, thereby redefining approaches to fluid dynamics analysis and design.