High-order computational fluid dynamics (CFD) solvers are increasingly important for high-fidelity simulations in science and engineering. However, their widespread adoption for large-scale applications remains limited by computational cost, scalability, and robustness challenges. This minisymposium will highlight recent developments in high-order CFD solvers that improve accuracy, efficiency, and stability while reducing overall time-to-solution. Particular emphasis will be placed on advances that leverage both discretization and solver technologies to reduce computational cost without compromising predictive capability. A central theme of the minisymposium is the development of spatial and temporal discretization strategies that enable scalable solvers on modern high-performance computing architectures. These approaches play a critical role in improving solver robustness, reducing iteration counts, enhancing parallel efficiency, and enabling efficient simulations at unprecedented scales. Topics include high-order discretizations for compressible and incompressible flows, adaptive methods, space-time formulations, and techniques that improve the conditioning and performance of large-scale solver frameworks. Contributions addressing provably stable discretizations, shock-capturing methodologies, and advanced time-integration schemes are also encouraged. The minisymposium will further explore the interaction between discretization design and linear and nonlinear solver performance, including advances in preconditioning and solver acceleration strategies. Applications demonstrating practical benefits for industrially relevant CFD problems are particularly welcome. Overall, the objective is to showcase developments that are making high-order CFD solvers more accurate, scalable, stable, and computationally affordable for the next-generation of predictive simulations.