Why does SVM not scale well with extremely large datasets?

SVM struggles with scalability due to quadratic kernel matrix computation.

Kernel SVM requires computing pairwise similarities between all training points, resulting in O(n²) memory and O(n³) time complexity in worst cases. This becomes impractical for large-scale datasets.