The algorithm has scalable memory requirements and can handle problems with many thousands of support vectors efficiently.
Many tasks have the property of sparse instance vectors.
This implementation makes use of this property which leads to a very compact and efficient representation. The source code is free for scientific use. It must not be modified and distributed without prior permission of the author. If you use SVM light in your scientific work, please cite as T. Joachims, Making large-Scale SVM Learning Practical. I will put you on my mailing list to inform you about new versions and bug-fixes.