# SKLearn KernelRidge memory demand

I am fitting a model with 100,000 samples x 10 features (6 ints and 4 floats), using SKLearn KernelRidge:

model = KernelRidge(kernel='linear')


Looking at the task manager, 'Python' process takes ~40GB.

Can you please explain why is there such a high demand? What kind of matrix is built in the background?