Most models have a per-head dimension much smaller than the input dimension, so it's faster to multiply by the small wk and wk individually than to multiply by the large matrix W. Also, if you use rotary positional embeddings, the RoPE matrices need to be sandwiched in the middle and they're different for every token, so you could no longer premultiply just once.