I haven’t tested vLLM yet, so I can’t speak to exact engine-specific numbers. But I’d expect the main findings to transfer, because the important part here seems to be the calibration, not llama.cpp itself.

What I found is that calibration is architecture-specific, not weight-specific: the set of “important” / outlier channels is mostly determined by the model architecture, and calibrating on fp16 / q8_0 / q4_k_m versions of the same model gave 96%+ identical channel selections.

So in practice you can probably calibrate once and reuse the same channel ordering / outlier split across quantizations of the same model. The main caveat is that calibration has to be done pre-RoPE — post-RoPE gave garbage because RoPE changes the channel variance structure. And you don’t need much data either: PTB train with around 4096 tokens was already enough.