This study aims to find a relation between the number of channels and the computational burden for specific absorption rate (SAR) calculation using virtual observation point-based SAR compression. Methods: Eleven different arrays of rectangular loops covering a cylinder of fixed size around the head of an anatomically correct voxel model were simulated. The resulting Q-matrices were compressed with 2 different compression algorithms, with the overestimation fixed to a certain fraction of worst-case SAR, median SAR, or minimum SAR. The latter 2 were calculated from 1e6 normalized random excitation vectors. Results:The number of virtual observation points increased with the number of channels to the power of 2.3-3.7, depending on the compression algorithm when holding the relative error fixed. Together with the increase in the size of the Q-matrices (and therefore the size of the virtual observation points), the total increase in computational burden with the number of channels was to the power of 4.3-5.7. Conclusion:The computational cost emphasizes the need to use the best possible compression algorithms when moving to high channel counts.