The paper describes a neural network architecture, based on **normalizing flows** alone, that is able to generate posteriors on the **full** $D = 15$ dimensional parameter space of quasi-circular binary inspirals, using input data surrounding the first observed GW event, **GW150914**, from multiple gravitational-wave detectors.