You can get huge performance benefits, where performance may also imply stability or convergence. For example, recently I had to make a parametric fit of a lar

You can get huge performance benefits, where performance may also imply stability or convergence. For example, recently I had to make a parametric fit of a large dataset to an exotic distribution. Hand-tuning maximum likelihood and rounding observations to lower precision, i.e. having a custom likelihood function, made it solvable and numerically stable. Using e.g. Julia, or JAX, you can get access to lower level primitives that are composable and let you do this relatively quickly, without needing to code everything from scratch, just whatever you are interested in changing.