A laptop GPU trained XGBoost 4.6-9x faster. Data prep was a draw.

I ran one feature-and-model recipe twice on the same fraud dataset: once on pandas with XGBoost on CPU, once on RAPIDS cuDF with XGBoost on CUDA. Model training was 4.6-9x faster on the GPU. Data preparation took about the same time on both. The numbers, and the limits of this measurement, are below. Setup Data: the IEEE-CIS Fraud Detection training set, all 590,540 transactions and 434 features. Recipe: CSV read, identity join, a 0.95 missingness filter on the V columns, categorical encoding, group-by aggregates, then XGBoost (200 trees) on a time-ordered 80/20 split. Same code on both engines, because cuDF follows the pandas API. Hardware: one laptop with an Intel i7-11800H (16 logical cores) and an NVIDIA RTX 3060 Laptop GPU (6 GB). Software: the GPU run used WSL2, Python 3.12, cuDF 26.10 and XGBoost 3.4.1. The CPU run used native Windows, Python 3.11, pandas 3.1.1 and XGBoost 3.2.0, because XGBoost 3.4.1 needs Python 3.12. Results Each cell is the range ov