A machine learning team is training a model on a multi-GPU system to process very long documents. They find that while the model's overall parameters fit in memory, the training process consistently fails with an 'out-of-memory' error specifically during the self-attention calculation step. The team proposes a solution where the Key (K) and Value (V) matrices, which are derived from the input sequence, are split row-wise into segments. Each segment pair (a segment of K and its corresponding segment of V) is then sent to a different GPU for processing. Analyze why this specific strategy of splitting and distributing the Key and Value matrices would resolve the 'out-of-memory' error. In your explanation, detail the relationship between this division of data and the computational workload on each individual GPU.