linfa-svm learning speed and memory allocation

Hello,

I cannot get linfa-svm to produce any result as the operations take way too much memory and time:

Multi-class classifier full data:

[2023-07-11T08:18:38Z INFO  be] Fit SVM classifier with #204242 training points
memory allocation of 333718356512 bytes failed
Aborted (core dumped)

Multi-class classifier limited data:

[2023-07-11T09:16:38Z INFO  be] Fit SVM classifier with #10214 training points

^C

real    120m7.427s
user    119m52.661s
sys     0m2.573s

Regressor:

[2023-07-11T11:46:22Z INFO  be] Fit SVM regressor with #10214 training points

(still running after 20 mins and will be probably running much, much longer)

I guess I could trim the input data set further but 10K input data points doesn't seem like way too many.
Is there any other way to speed things up?

Start with the linfa-svm SVM classifier and regressor fitting paths using the reported 10,214 and 204,242 training-point cases. Measure runtime and memory, then identify why fitting is impractical at those sizes. Done means multi-class classification and regression complete without the reported allocation failure or excessive runtime.