Modern AI systems are defined as much by their infrastructure as by their algorithms. Training the largest models requires hardware capable of sustaining trillions of operations, communication protocols that synchronize thousands of devices, and frameworks that make these resources usable. Scaling training is therefore a central concern in contemporary deep learning.