XGBoost is a widely used gradient boosting algorithm that can be slow to train due to its computational requirements. To accelerate training, one approach is to change the tree construction method, such as using the GPU implementation of the histogram-based algorithm (`gpu_hist`) which can significantly reduce training time on larger datasets. Another approach is to leverage cloud computing by utilizing more resources than available locally, with XGBoost having support for GPUs on some operating systems. Additionally, distributing XGBoost model training with XGBoost-Ray, a distributed execution framework that leverages the actor model, can also speed up training by scaling applications and leveraging state-of-the-art machine learning libraries.