---
title: "Topic Modeling"
publisher: "nvidia"
type: "playbook"
updated: "2026-03-09T17:29:33.565Z"
description: "Extract insights from massive text datasets using cuML's GPU-accelerated BERTopic"
canonical: "https://build.nvidia.com/station/topic-modeling.md"
---

# Basic idea

Topic modeling helps you discover hidden themes in large document collections—but traditional methods crawl when datasets grow to millions of records. This playbook shows how to process **40 million Amazon product reviews in minutes** using GPU-accelerated BERTopic.

BERTopic combines transformer embeddings with clustering to extract human-readable topics from text. By swapping CPU-based UMAP and HDBSCAN with GPU-accelerated versions from **RAPIDS cuML**, you get the same results dramatically faster—no code changes required.

- **Drop-in GPU acceleration**: Load `cuml.accel` and your existing UMAP/HDBSCAN code runs on GPU automatically
- **Scale to millions**: Process datasets that would take hours on CPU in minutes on GPU
- **Interactive visualizations**: Explore topic distributions, relationships, and document clusters

# What you'll accomplish

You'll run a complete topic modeling pipeline on 40 million product reviews and generate interactive visualizations of discovered topics.

By the end, you'll be able to:
- Use cuML's drop-in accelerators for UMAP and HDBSCAN
- Generate sentence embeddings at scale with SentenceTransformers
- Create topic visualizations including heatmaps, barcharts, and document datamaps

# What to know before starting

- Experience with Python and Jupyter notebooks
- Basic understanding of machine learning concepts (embeddings, clustering)
- Familiarity with pandas DataFrames

# Prerequisites

**Hardware Requirements:**
- NVIDIA DGX Station with GB300 GPU
- Minimum 64GB GPU memory for processing 40M documents
- At least 50GB available storage for dataset and embeddings

**Software Requirements:**
- Conda (Miniconda or Anaconda): `conda --version`
- CUDA 13.0 compatible drivers: `nvidia-smi`
- Network access to download the Amazon Reviews dataset (~14GB compressed)

# Ancillary files

All required assets are in the playbook directory `nvidia/station-topic-modeling/assets` (see [Instructions](https://build.nvidia.com/station/topic-modeling/instructions), Step 7). Key file:

- `video_notebook_for_GPU_Accelerated_Machine_Learning_BERTopic_RTX6000_40M.ipynb` - Complete Jupyter notebook with GPU-accelerated topic modeling pipeline (filename reflects original demo hardware; the notebook runs on GB300 and other NVIDIA GPUs)

# Time & risk

* **Estimated time:** 45 minutes (includes environment setup, dataset download, and embedding generation)
* **Risk level:** Low
* Large dataset download (~14GB) may take time depending on network speed
* Embedding generation requires significant GPU memory
* **Rollback:** Delete the downloaded dataset and any generated embedding files to restore state
* **Last Updated:** 03/02/2026
* First Publication

## More

- [Instructions](/station/topic-modeling/instructions.md)