What likely causes a bottleneck in deep learning training speed when using a GPU cluster?

The identified cause, insufficient PCIe bandwidth between GPUs and the CPU, is a pivotal factor contributing to bottlenecks in deep learning training speed when utilizing a GPU cluster. PCIe (Peripheral Component Interconnect Express) bandwidth is crucial for enabling rapid data transfer between the CPU and multiple GPUs. In deep learning tasks, large amounts of data and parameters need to be communicated between the CPU (which may be coordinating data preparation and model orchestration) and the GPUs (which are performing the heavy computations). When the bandwidth is insufficient, this data transfer can slow down the overall training process, preventing the GPUs from operating at their maximum potential due to idle times while waiting for data.

This bottleneck can occur regardless of the number or processing power of the GPUs themselves, as it directly impacts the efficiency of data movement between components of the architecture. High-performance GPUs can become underutilized if the data pipeline is not optimized or if the connections between CPUs and GPUs cannot handle the volume of data required promptly.

The other options deal with different aspects of deep learning setups, such as RAM limitations and model complexity, which although relevant, do not specifically address the crucial interface limiting speed in a training scenario involving distributed GPUs.