In this talk, we describe a Julia implementation of RipQP, a regularized interior-point method for convex quadratic optimization. RipQP is able to solve problems in several floating-point formats, and can also start in a lower precision as a form of warm-start. The algorithm uses sparse factorizations or Krylov methods from the Julia package Krylov.jl. We present an easy way to use RipQP to solve problems modeled with QuadraticModels.jl and LLSModels.jl.