^ This.

Intent is the answer and AI has none.

This is all quantifiable. I regularly have my model run benchmarks against all the config permutations and then choose the best based on my criteria, which typically boil down to trading prefill and decode times

What model has to trade between those? I have both. You just have different, independently-optimized forward passes for each.

I'm running on Strix Halo so memory bandwidth is my constraint. In that example I'm describing the choice between using ROCm or Vulkan. I have a llama-swap config that can call different instances of llama-server running a toolbox with either runtime.

Memory bandwidth is my constraint too (M4 Max) but prefill and single-token decode don't run at the same time. It's best to use batched prefill so you can benefit from processing multiple tokens with a single pass through the model weights.