CMR multitasking is promising for quantitative cardiac imaging, and can achieve fast three-slice quantification when combined with simultaneous multi-slice (SMS) acquisition. However, slow non-Cartesian iterative reconstruction is a barrier to clinical adoption. Deep learning can accelerate reconstruction, but the SMS training data are currently limited. Here we propose a data-consistent deep subspace transfer learning strategy that trains on single-slice T1 CMR multitasking data but is applied to SMS-encoded T1 CMR multitasking image reconstruction. The proposed strategy is >40x faster than the conventional SMS reconstruction, resulting in an equally better image quality and comparably precise T1 as in single-slice reconstruction.