Why would you ever want to leave the warm, fuzzy embrace of torch.nn? It works, it’s differentiable, and it rarely causes your entire Python session to segfault without a stack trace. The answer usually comes down to the “Memory Wall.” Modern deep learning is often less bound by how fast your GPU can do math (FLOPS) and more bound by how fast it can move data around (Memory Bandwidth). When you write a sequence of simple PyTorch operations, something like x = x * 2 + y the GPU often reads x from memory, multiplies it, writes it back, reads it again to add y, and writes it back again. It’s the computational equivalent of making five separate trips to the grocery store because you forgot the eggs, then the milk, then the bread. Writing a custom kernel lets you “fuse” these operations. You load the data once, perform a dozen mathematical operations on it while it sits in the ultra-fast chip registers, and write it back once. The performance gains can be massive (often 2x-10x for specific layers).But traditionally, the “cost” of accessing those gains, learning C++, understanding warp divergence, and manual memory management, was just too high for most researchers. That equation is finally changing.