With infinite individuals, yes, but with finite samples you’re not going to get posteriors that can/should be lumped together.
This is generally not recommended as you inevitably end up having to summarize the posterior to turn it into a prior causing information loss and unreliable propagation of uncertainty thereby.
Have you tried the options for within-chain parallelization? Also see here for a speed-up trick that’s often helpful for hierarchical models with highly-redundant design matrices.