One of the key advantages of the Transformer, Karpathy explained, is its ability to scale gracefully with increased computational resources. As the amount of compute power dedicated to the Transformer model is increased, the quality of its outputs improves dramatically, often to the point of producing lifelike, high-fidelity results. This scaling property, known as the "scaling laws," is a hallmark of the Transformer and a testament to its versatility.
Karpathy attributed the Transformer's success to a combination of several innovations, including residual connections, layer normalization, the attention mechanism, and the absence of saturating nonlinearities. These elements, when combined, have created a "magical" piece of technology that can be trained to perform a wide variety of tasks.