What is the most likely cause of memory overflows in a GPU during AI model training?

While the idea of fragmented memory may seem relevant in the context of GPU memory overflows, the most likely cause of memory overflows during AI model training is when the model’s batch size is too large. A larger batch size requires more memory to hold the data being processed, as well as the model weights, gradients, and any intermediate values needed during backpropagation. When the collective memory consumption exceeds the available GPU memory, an overflow occurs.

Memorization of larger datasets within more extensive neural networks typically escalates the dynamic memory allocation, which can lead to insufficient resources being available for training. Additionally, batch sizes dictate how many samples are processed together, which directly influences the peak memory usage at any given time during training.

Other options like fragmented memory might contribute to inefficiencies or performance degradation but are less likely to be the primary cause of memory overflow. Data throughput issues or CPU overloads can impact performance but do not directly correlate with memory overflow in the same immediate manner as batch size does. Thus, while reducing batch size is a common and effective method to mitigate memory overflow, it's crucial to understand the specific demands placed on GPU memory during training tasks.