This thesis investigates how temporal design choices affect the real-time feasibility of human motion prediction models. Two state-of-the-art models were evaluated: GCNext, a data-driven graph convolutional model, and PhysMoP, a hybrid model combining a physics-based and data-driven branch. Controlled experiments showed the influence of input history length, temporal resolution, and the model architecture on prediction accuracy and latency. Results showed that longer observation windows do not necessarily improve accuracy, while increasing the latency. Both models were sensitive to changes in temporal resolution, as they implicitly assumed a fixed sampling rate. Real-time performance analysis indicated that single-pass architectures were favoured, while autoregressive models suffered from compounding delay. Retraining GCNext with shorter input histories and optimising autoregressive passes achieved substantial latency reduction with minimal accuracy loss. These results show that temporal configurations are critical design choices for achieving real-time feasibility of human motion prediction models. The code for this paper is available at https://github.com/AndrewHutani/HMP