ROCm-Finance: ROCm toolkit for finance

ROCm-Finance pulls the trajectory of tomorrow into today: an open toolkit on the ROCm stack that delivers GPU-native gradient-boosting stacks that the industry already trusts. XGBoost, LightGBM, and ThunderGBM, tuned for AMD Instinct accelerators, so training, scoring, and simulation work land closer to real time than the CPU-era baselines could achieve.

ROCm-Finance collapses the distance between signal and decision. The same workloads that once queued overnight now run in minutes. Risk, fraud detection, forecasting, and simulation pipelines step into the high-bandwidth GPU computing ROCm was built to serve. ROCm-Finance provides production-oriented kernels, memory paths, and scaling behavior so your boosting jobs feel like they arrived from the next generation, even on this week’s cluster.

For more information on ROCm-Finance, including comparisons, prerequisites, installation, and deep API reference, see the ROCm-Finance documentation.

XGBoost

General-purpose GPU gradient boosting. Start here for high-performance workloads for data-intensive applications.

LightGBM

Leaf-wise training. Strong fit when sparsity abounds and dataset size drives the bottleneck.

ThunderGBM

GPU-oriented boosting for highly parallel, GPU-intensive training and simulation-style runs on high-dimensional datasets.

GitHub

Source for all ROCm-Finance libraries on GitHub.

Examples

Runnable examples on GitHub to explore the code.

ROCm-Finance Blogs

Browse blogs detailing how to accelerate your finance workloads using gradient boosting on AMD Instinct GPUs.