Spent two weeks on a kernel that benchmarked 29x faster. End to end it's maybe 6-10%, and it's not even wired in yet.

Explain Like I'm Normal

A developer spent weeks hand-coding an AVX-512 kernel to boost BitNet ternary model inference by 29x, only to realize the performance is capped by DRAM bandwidth. While the compute speed skyrocketed, the actual end-to-end gain is minimal because the CPU is stuck waiting for data from memory. This is a classic lesson in why AI performance bottlenecks are often about moving data, not just calculating it.