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# ROCm-Finance Blogs

This page contains a list of blog posts related to the AMD ROCm™ Finance Toolkit (ROCm-Finance)
and its individual components. See [ROCm Blogs](https://rocm.blogs.amd.com/) for
blogs related to all AMD and ROCm products.

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<a href="https://rocm.blogs.amd.com/artificial-intelligence/xgboost-multi-gpu/README.html" class="card-header-link">
<h2 class="card-header">Accelerating XGBoost with Dask using multiple AMD GPUs</h2>
</a>
<p class="paragraph"> XGBoost is an optimized library for distributed gradient boosting. It has become the leading machine learning library for solving regression and classification problems.</p>
:::

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<a href="https://rocm.blogs.amd.com/ecosystems-and-partners/rocm-finance/README.html" class="card-header-link">
<h2 class="card-header">Using Gradient Boosting Libraries on MI300X for Financial Risk Prediction</h2>
</a>
<p class="paragraph"> Financial institutions deal with massive datasets and complex models that demand high computational power.
With AMD ROCm™ and GPU-accelerated libraries like LightGBM and ThunderGBM, we can significantly reduce training time while improving model performance.</p>
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# ROCm-Finance: ROCm toolkit for finance

ROCm-Finance pulls the trajectory of tomorrow into today: an open toolkit on the [ROCm](https://rocm.docs.amd.com/) stack that
delivers GPU-native gradient-boosting stacks that the industry already trusts. XGBoost,
LightGBM, and ThunderGBM, tuned for [AMD Instinct](https://www.amd.com/en/products/accelerators/instinct.html)
accelerators, so training, scoring, and scenario work land closer to real time than the CPU-era
baselines could achieve.
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ROCm-Finance collapses the distance between signal and decision. The same workloads that once
queued overnight now run in minutes. Risk, fraud, forecasting, and simulation pipelines step
into the high-bandwidth, multi-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](https://rocm.docs.amd.com/projects/rocm-finance/en/latest/index.html).

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<a href=./xgboost.html class="card-header-link">
<h2 class="card-header">XGBoost</h2>
</a>
<p class="paragraph"> General-purpose GPU gradient boosting. Start here for many finance tabular workflows.
</p>
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<a href=./lightgbm.html class="card-header-link">
<h2 class="card-header">LightGBM</h2>
</a>
<p class="paragraph"> Leaf-wise training. Strong fit when dataset size drives the bottleneck.
</p>
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<a href=./thundergbm.html class="card-header-link">
<h2 class="card-header">ThunderGBM</h2>
</a>
<p class="paragraph"> GPU-oriented boosting for highly parallel, GPU-intensive training and simulation-style runs.
</p>
::::

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<a href="https://github.com/ROCm/ROCm-Finance" class="card-header-link">
<h2 class="card-header">GitHub</h2>
</a>
<p class="paragraph"> Source for ROCm-Finance and related packaging on GitHub.
</p>
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<a href="https://github.com/ROCm/rocm-finance/tree/release/26.01/examples" class="card-header-link">
<h2 class="card-header">Examples</h2>
</a>
<p class="paragraph"> Runnable examples on GitHub to explore the code.
</p>
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<a href=./finance-blogs.html class="card-header-link">
<h2 class="card-header">ROCm-Finance Blogs</h2>
</a>
<p class="paragraph"> Browse blogs detailing how to accelerate your finance boost workloads on AMD Instinct GPUs.
</p>
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# LightGBM (ROCm-Finance)

LightGBM is how ROCm-Finance answers scale: leaf-wise training that shines when dataset size—wide
feature stores, long histories, dense microstructure matrices—would otherwise push
decisions into the next shift. On Instinct, that wall between overnight queues and minutes
thins out; the same boosting idiom, routed through ROCm's high-bandwidth, multi-GPU environment.

Reach for LightGBM when volume is the bottleneck, and you still want gradient boosting semantics
with GPU-native backing.

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<a href="https://rocm.docs.amd.com/projects/lightgbm/en/latest/" class="card-header-link">
<h2 class="card-header">Documentation</h2>
</a>
<p class="paragraph"> Installation instructions, how-to guides, and API reference material are on the ROCm Documentation site.
</p>
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<a href="https://github.com/ROCm/LightGBM/" class="card-header-link">
<h2 class="card-header">Github</h2>
</a>
<p class="paragraph"> View the LightGBM source code on Github.
</p>
:::

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# ThunderGBM (ROCm-Finance)

Use ThunderGBM when parallelism and raw throughput dominate the story:
massively parallel trees, simulation-scale batches, and scenario grids that want the accelerator
to do the heavy lifting today—not in some speculative later hardware generation. ThunderGBM collapses the
signal-to-decision distance, optimized for highly parallel,
GPU-intensive training runs on [AMD Instinct](https://www.amd.com/en/products/accelerators/instinct.html) silicon.

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<a href="https://rocm.docs.amd.com/projects/thundergbm/en/latest/" class="card-header-link">
<h2 class="card-header">Documentation</h2>
</a>
<p class="paragraph"> ThunderGBM on ROCm—component documentation on the ROCm Documentation site.
</p>
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<a href="https://github.com/ROCm/ThunderGBM/" class="card-header-link">
<h2 class="card-header">Github</h2>
</a>
<p class="paragraph"> View the ThunderGBM source code on Github.
</p>
:::

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# XGBoost (ROCm-Finance)

XGBoost is the general-purpose engine in ROCm-Finance: tabular risk, fraud, pricing-side features, and trading-adjacent workloads.
Use it when you want a familiar level-wise boosting path with broad finance coverage and a
straightforward on-ramp from yesterday's pipelines to tomorrow's throughput.

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<a href="https://rocm.docs.amd.com/projects/xgboost/en/latest/" class="card-header-link">
<h2 class="card-header">Documentation</h2>
</a>
<p class="paragraph"> XGBoost on ROCm—installation, tuning, and API reference on the ROCm Documentation site.
</p>
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<a href="https://github.com/ROCm/XGBoost/" class="card-header-link">
<h2 class="card-header">Github</h2>
</a>
<p class="paragraph"> View the XGBoost source code on Github.
</p>
:::

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---
# AMD Instinct Data Center GPU Documentation

The AMD Instinct Documentation site provides comprehensive guides and technical documentation for system administrators and technical users deploying AMD Instinct Data Center GPUs in enterprise environments. This site focuses on large-scale deployment, cluster management, monitoring, and operational best practices for both HPC and AI workloads. For API documentation and core software stack details, please visit the [ROCm documentation](https://rocm.docs.amd.com).
The AMD Instinct Documentation site provides comprehensive guides and technical documentation for system administrators and technical users deploying AMD Instinct Data Center GPUs in enterprise environments. This site focuses on large-scale deployment, cluster management, monitoring, and operational best practices for both HPC and AI workloads. For API documentation and core software stack details, visit the [ROCm documentation](https://rocm.docs.amd.com).

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<a href=./finance/index.html class="card-header-link">
<h2 class="card-header">ROCm-Finance</h2>
</a>
<p class="paragraph"> Boost your financial workloads with the ROCm Toolkit for Finance (ROCm-Finance).
</p>
:::

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<a href=./finance/xgboost.html class="card-header-link">
<h2 class="card-header">XGBoost</h2>
</a>
<p class="paragraph"> General-purpose GPU gradient boosting. Start here for many finance tabular workflows.
</p>
:::

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<a href=./finance/lightgbm.html class="card-header-link">
<h2 class="card-header">LightGBM</h2>
</a>
<p class="paragraph"> Leaf-wise training is the way to go. It is a strong fit when dataset size drives the bottleneck.
</p>
:::

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<a href=./finance/thundergbm.html class="card-header-link">
<h2 class="card-header">ThunderGBM</h2>
</a>
<p class="paragraph"> GPU-oriented boosting for highly parallel, GPU-intensive training and simulation-style runs.
</p>
:::

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<a href="https://github.com/ROCm/ROCm-Finance" class="card-header-link">
<h2 class="card-header">GitHub</h2>
</a>
<p class="paragraph"> Source for ROCm-Finance and related packaging on GitHub.
</p>
:::

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<a href="https://github.com/ROCm/rocm-finance/tree/release/26.01/examples" class="card-header-link">
<h2 class="card-header">Examples</h2>
</a>
<p class="paragraph"> Runnable examples on GitHub to explore the code.
</p>
:::

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<a href=./finance/finance-blogs.html class="card-header-link">
<h2 class="card-header">ROCm-Finance Blogs</h2>
</a>
<p class="paragraph"> Browse blogs detailing how to accelerate your finance boost workloads on AMD Instinct GPUs.
</p>
:::

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