Ai Helping Build Better Ai How Agents Accelerate Liger Kernel Engineering

LinkedIn's Liger Kernel project uses coding agents to build specialized GPU routines, with human review before implementation proceeds. An agent first describes the operation and its implementation choices, then generates code and runs correctness and performance checks. That intermediate description gives reviewers a chance to catch a mistaken engineering decision before it spreads through the implementation.

In one reported example, the resulting ReLU Squared routine ran its forward computation 1.9 times faster and its backward computation 3.2 times faster than the PyTorch baseline. Those are LinkedIn's results for that routine; the reusable contribution is a workflow that combines generation with explicit verification.