Scaling AI: The Communication Wall

As AI models grow to trillions of parameters, the communication between thousands of chips becomes a critical bottleneck in training and inference. Data transfer speeds vary greatly depending on the proximity of compute units, with longer distances incurring higher latency and energy costs. Scaling compute power alone doesn't guarantee speedup due to synchronization overheads that worsen with system size, limiting performance gains.