  We show how to efficiently sample the joint posterior of two non-covariant
experiments with a large set of nuisance parameters. Specifically, we train an
ensemble of normalizing flows to learn the the posterior distribution of both
experiments. Once trained, we can use the flows to draw $\mathcal{O} (10^9)$
samples from the joint posterior of two independent cosmological measurements
in seconds -- saving up to $\mathcal{O}(1)$ ton of $\text{CO}_2$ per Monte
Carlo run. Using this new technique we find joint constraints between the Dark
Energy Survey $3 \times 2$ point measurement, South Pole Telescope and Planck
CMB lensing and a BOSS direct fit full shape analyses, for the first time. We
find $\Omega_{\rm m} = 0.32^{+0.01}_{-0.01}$ and $S_8 = 0.79 ^ {+0.01}_
{-0.01}$. We release a public package called {\tt CombineHarvesterFlow}
(https://github.com/pltaylor16/CombineHarvesterFlow) which performs these
calculations.
