Most compilers convert all iterations of a vectorizable loop into vector operations to decrease processing time. This pa- per proposes Scalar Interpolation, a technique that inserts scalar operations into vectorized loops to increase the utiliza- tion of execution units in processors with distinct pipelines for scalar and vector processing. Scalar interpolation inserts scalar operations for an entire iteration of the sequential loop to avoid data movements between vector and scalar registers. A challenge to introducing scalar interpolation is creating a static cost model to guide the compiler’s decision to interpolate scalar operations in a loop. An alternative to a static cost model is to perform auto-tuning in a loop to dy- namically discover a sweet spot for the scalar interpolation factor. A performance study on an LLVM-based prototype reveals speedups of up to 30% on Intel Xeon (x86) with a static analysis of the cost model, and 43% on Kungpeng-920 (AArch64) with auto-tuning.