Recent findings indicate that GPU matrix multiplications, the core operation for AI workloads, execute significantly faster when processing data with predictable access patterns. This optimization minimizes memory bottlenecks and maximizes compute efficiency, ultimately delivering substantial improvements in flops per watt for large-scale machine learning models.