A lot of CPU inference libraries (llama.cpp included) use as much SIMD as possible, sometimes by hand-writing loops. The one I hack on, llama.rs, uses portable_simd but specializes to your CPU at compile time.

My experience has been that most CPU inference is actually not compute limited, but memory bandwidth limited, since most weights are used for a few operations per token (how quickly can you load and unload the entire 70 GB of weights into your registers?). It's not quite that bad but I found most vectorization changes didn't meaningfully change performance.

Most inference builds on top of BLAS libraries, which in their implementation take advantage of SIMD.