In many science and engineering applications, problems may result in solving a sparse linear system AX=B. For example, SuperLU_MCDT, a linear solver, was used for the large penta-diagonal matrices for 2L) problems and hepta-diagonal matrices for 3D problems, coming from the incompressible blood flow simulation (see ). It is important to test the status and potential improvements of state-of-the-art solvers on new technologies. In this work, sequential, multithreaded and distributed versions of SuperLU solvers (see ) are examined on the Intel Xeon Phi coprocessors using offload programming model at the EURORA cluster of CINECA in Italy. We consider a portfolio of test matrices containing patterned matrices from LTEMM () and randomly located matrices. This architecture can benefit from high parallelism and large vectors. We find that the sequential Supertti benefited up to 45 % performance improvement from the offload programming depending on the sparse matrix type and the size of transferred and processed data.