Accurate plasma state reconstruction will be crucial for the success of ITER and future fusion plants, but the harsh conditions of a burning plasma will make diagnostic operation more challenging than in current machines. Integrated data analysis (IDA) based on Bayesian inference allows for improved information gain by combining the analysis of many diagnostics into a single step using sophisticated forward models. It also provides a framework to seamlessly combine predictive modeling and data, which can be invaluable in a data-poor environment. As a step towards IDA at scale, we present a new, fast integrated analysis framework that allows for the simultaneous reconstruction of the kinetic profiles and the magnetic equilibrium with uncertainties included. This analysis framework allows for the systematic evaluation of models using extensive experimental data leveraging DOE supercomputing infrastructure, such as being developed through the DOE-Advanced Scientific Computing Research integrated research infrastructure (Smith, XLOOP). To test the performance and verify the code it was applied to an ITER-like scenario using a realistic machine geometry and diagnostic description. Using artificial data for magnetics, Thomson scattering, interferometry, and polarimetry generated from a known ground truth, the coupled equilibrium and kinetic profile reconstruction problem was solved via the Maximum a posteriori method in approximately three minutes on a multicore CPU including uncertainty quantification. The resulting equilibrium and kinetic profiles were found to be in reasonable agreement with the ground truth.