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An investigation into why dual RTX 5060 Ti GPUs max out at ~50% utilization when running large LLMs like Qwen 27B reveals that memory bandwidth is the bottleneck and layer-by-layer splitting causes idle time, making it a relay race rather than parallel computation.
QSplitFL proposes a DQN-based framework for optimal split point selection in split federated learning, using client hardware metrics to adapt to heterogeneous devices. Experiments show improved convergence and accuracy across multiple datasets and architectures.