NVIDIA XGBoost 3.0: Training Terabyte-Scale Datasets with Grace Hopper Superchip

NVIDIA XGBoost 3.0: Training Terabyte-Scale Datasets with Grace Hopper Superchip

NVIDIA has unveiled a major milestone in scalable machine learning: XGBoost 3.0, now able to train gradient-boosted decision tree (GBDT) models from gigabytes up to 1 terabyte (TB) on a single GH200 Grace Hopper Superchip. The breakthrough enables companies to process immense datasets for applications like fraud detection, credit risk modeling, and algorithmic trading, simplifying the once-complex process of scaling machine learning ML pipelines…