This review discusses the computational challenges and breakthroughs in large-scale photonic inverse design. Recent advancements in inverse design approaches, exemplified by their large-scale optimization of all geometrical degrees of freedom, have significantly shifted the paradigm in photonic design. However, these strategies still require full-wave Maxwell solutions to compute gradients concerning the desired figure of merit, imposing prohibitive computational demands on conventional computing platforms. The review analyzes the computational challenges associated with the design of large-scale photonic structures, examining the adequacy of various electromagnetic solvers, from conventional to neural network-based solvers, and their suitability and limitations. It also evaluates optimization techniques, analyzes their advantages and disadvantages in large-scale applications, and highlights cutting-edge studies combining neural networks with inverse design for large-scale applications. The review aims to provide insights into navigating the landscape of large-scale design and advocate for strategic advancements in optimization methods, solver selection, and the integration of neural networks to overcome computational barriers, thereby guiding future advancements in large-scale photonic design. Key challenges in large-scale simulations include memory constraints, computational throughput, and memory transfer bandwidth. These limitations significantly affect the maximum feasible size of simulations. For example, simulating a 50 μm² metasurface with a 5 nm mesh size using the finite-difference time-domain (FDTD) method may require approximately 100 hours of simulation time and 100 GB of memory. The computational performance is also constrained by the memory transfer bandwidth of contemporary computing systems, which can lead to bottlenecks in data transfer and processing. Despite these challenges, recent studies have focused on harnessing GPU technology to enhance computational speeds, offering significant improvements in simulation efficiency. However, GPU memory remains a persistent challenge due to its higher cost and limited capacity. The review also explores various computational electromagnetic solvers suitable for large-scale problems, including the finite element method (FEM), finite-difference time-domain (FDTD), and rigorous coupled-wave analysis (RCWA). These methods are essential for designing and investigating photonic systems, offering cost-effective alternatives to physical experimentation. Additionally, the integration of neural networks into computational electromagnetics has shown promise in approximating solutions to Maxwell's equations with reduced computational overhead, offering efficient alternatives to traditional EM simulations. The review highlights the potential of deep learning-based surrogate solvers, such as WaveY-Net, MaxwellNet, and Fourier neural operators (FNOs), in significantly streamlining the simulation process and improving computational efficiency. The review also discusses hardware acceleration and parallel computing strategies, emphasizing the importance of distributing complex problems across multiple processors to reduce simulation time and memory requirements. Commercial and open-source tools, such as Lumerical, COMSOL, and Meep, have integrated parallelization features, simplifying the implementation process. Furthermore, the development of cloud-based tools, such as PlanOpSim, has enabled researchers to create large-scale designs without relying on high-performance computers, significantly reducing the barriers for advanced photonicThis review discusses the computational challenges and breakthroughs in large-scale photonic inverse design. Recent advancements in inverse design approaches, exemplified by their large-scale optimization of all geometrical degrees of freedom, have significantly shifted the paradigm in photonic design. However, these strategies still require full-wave Maxwell solutions to compute gradients concerning the desired figure of merit, imposing prohibitive computational demands on conventional computing platforms. The review analyzes the computational challenges associated with the design of large-scale photonic structures, examining the adequacy of various electromagnetic solvers, from conventional to neural network-based solvers, and their suitability and limitations. It also evaluates optimization techniques, analyzes their advantages and disadvantages in large-scale applications, and highlights cutting-edge studies combining neural networks with inverse design for large-scale applications. The review aims to provide insights into navigating the landscape of large-scale design and advocate for strategic advancements in optimization methods, solver selection, and the integration of neural networks to overcome computational barriers, thereby guiding future advancements in large-scale photonic design. Key challenges in large-scale simulations include memory constraints, computational throughput, and memory transfer bandwidth. These limitations significantly affect the maximum feasible size of simulations. For example, simulating a 50 μm² metasurface with a 5 nm mesh size using the finite-difference time-domain (FDTD) method may require approximately 100 hours of simulation time and 100 GB of memory. The computational performance is also constrained by the memory transfer bandwidth of contemporary computing systems, which can lead to bottlenecks in data transfer and processing. Despite these challenges, recent studies have focused on harnessing GPU technology to enhance computational speeds, offering significant improvements in simulation efficiency. However, GPU memory remains a persistent challenge due to its higher cost and limited capacity. The review also explores various computational electromagnetic solvers suitable for large-scale problems, including the finite element method (FEM), finite-difference time-domain (FDTD), and rigorous coupled-wave analysis (RCWA). These methods are essential for designing and investigating photonic systems, offering cost-effective alternatives to physical experimentation. Additionally, the integration of neural networks into computational electromagnetics has shown promise in approximating solutions to Maxwell's equations with reduced computational overhead, offering efficient alternatives to traditional EM simulations. The review highlights the potential of deep learning-based surrogate solvers, such as WaveY-Net, MaxwellNet, and Fourier neural operators (FNOs), in significantly streamlining the simulation process and improving computational efficiency. The review also discusses hardware acceleration and parallel computing strategies, emphasizing the importance of distributing complex problems across multiple processors to reduce simulation time and memory requirements. Commercial and open-source tools, such as Lumerical, COMSOL, and Meep, have integrated parallelization features, simplifying the implementation process. Furthermore, the development of cloud-based tools, such as PlanOpSim, has enabled researchers to create large-scale designs without relying on high-performance computers, significantly reducing the barriers for advanced photonic