In the context of lunar exploration missions, autonomous navigation is essential to limit reliance on human intervention, which is hindered by significant communication delays and limited bandwidth. However, due to constrained on-board computational resources, complex robot-soil interactions, and the inherent challenges of full autonomy, most lunar rovers are currently limited to speeds of around 10cm/s. In this work, we investigate the use of Model Predictive Path Integral Control (MPPI) for high-speed navigation on uneven and cluttered lunar terrain. To this end, we develop an MPPI controller using the WARP framework, allowing to easily leverage the inherent parallelisation capabilities of GPUs. Experiments were conducted with a simulated Husky rover navigating realistic lunar terrain generated from NASA data, demonstrating the effectiveness of our motion planner. Since most MPPI implementations assume flat ground when sampling trajectories, this simplification can introduce inaccuracies, resulting in suboptimal, and sometimes even risky paths. To address this limitation, we propose a lightweight mathematical method for 3D trajectory projection, and evaluate its performance against the standard 2D MPPI approach. Overall, simulation results show that our 3D-enhanced MPPI significantly outperforms its 2D counterpart in terms of both traversal speed and obstacle avoidance on sloped, rock-scattered terrain. On flat ground, however, the traditional 2D MPPI still exhibits a high level of robustness. These findings suggest that an adaptive strategy, dynamically switching between 2D and 3D trajectory projection based on local terrain features, could offer a valuable trade-off between performance and power efficiency.