When I should use PCA? I have a data set with 60000 rows and 32 columns. I want to use SVM (with some more constraints that make it more complicated)and I think 32 columns are too large. So I decided to use PCA. But when I load PCA, the first 20 component describe 85% of data which 20 variables still is too large I think, but it is better than 32.
I am wondering is that ok if I use PCA? when n>>m? if not what is a better option?
If you can, go for some non-linear dimensionality reduction technique. The most powerful are Autoencoders, but you can also use t-SNE or other manifold techniques.

The problem of PCA is that it can extract only latent factors that are linearly associated with your variables. Using non-linear techniques, less variables can let you capture more of the original variance.