  We report a gravitational-wave parameter estimation algorithm, AMPLFI, based
on likelihood-free inference using normalizing flows. The focus of AMPLFI is to
perform real-time parameter estimation for candidates detected by
machine-learning based compact binary coalescence search, Aframe. We present
details of our algorithm and optimizations done related to data-loading and
pre-processing on accelerated hardware. We train our model using binary
black-hole (BBH) simulations on real LIGO-Virgo detector noise. Our model has
$\sim 6$ million trainable parameters with training times $\lesssim 24$ hours.
Based on online deployment on a mock data stream of LIGO-Virgo data, Aframe +
AMPLFI is able to pick up BBH candidates and infer parameters for real-time
alerts from data acquisition with a net latency of $\sim 6$s.
