Which two features of GPU architecture enhance their suitability for training large-scale AI models?

The choice highlighting the higher core density of GPUs and their optimization for floating-point operations is accurate in explaining why GPUs are particularly well-suited for training large-scale AI models.

GPUs are designed with significantly more cores as compared to CPUs, allowing them to perform many parallel operations simultaneously. This is crucial when training AI models, which often involve handling vast amounts of data and numerous mathematical computations that can be performed concurrently. The high core density enables GPUs to manage these operations more efficiently, resulting in faster computation times.

Additionally, optimization for floating-point operations allows GPUs to execute complex mathematical functions, such as those commonly found in neural network training, with greater speed and fidelity. This is critical because neural networks rely heavily on these types of calculations to adjust their weights and biases during the learning process.

In contrast, other features mentioned, such as higher clock speeds, may not contribute as significantly to the performance benefits seen in parallel tasks associated with AI model training. While power consumption is a factor in overall operational efficiency, it does not directly relate to the performance enhancements needed for executing large-scale computations in AI training. Thus, the combination of higher core density and optimized floating-point operation capabilities distinctly mark GPUs as a superior choice for this purpose.