  We present a lightweight, flexible, and high-performance framework for
inferring the properties of gravitational-wave events. By combining likelihood
heterodyning, automatically-differentiable and accelerator-compatible
waveforms, and gradient-based Markov chain Monte Carlo (MCMC) sampling enhanced
by normalizing flows, we achieve full Bayesian parameter estimation for real
events like GW150914 and GW170817 within a minute of sampling time. Our
framework does not require pretraining or explicit reparameterizations and can
be generalized to handle higher dimensional problems. We present the details of
our implementation and discuss trade-offs and future developments in the
context of other proposed strategies for real-time parameter estimation. Our
code for running the analysis is publicly available on GitHub
https://github.com/kazewong/jim.
