NVIDIA RAPIDS Slashes ML Training Time—No Code Required

Zero-config acceleration meets brute-force performance—while Wall Street still tries to explain AI to shareholders.

How it works: GPU-powered libraries automate the heavy lifting, turning days of tweaking into minutes of execution. No PhD required—just raw speed.

The kicker? Benchmarks show 15-50x faster data processing versus CPU-bound workflows. Meanwhile, hedge funds are still overpaying for ’AI-powered’ Excel macros.

NVIDIA has unveiled significant advancements in its RAPIDS software suite, focusing on machine learning acceleration and performance enhancements. According to NVIDIA, the latest updates introduce zero-code-change acceleration for Python machine learning, substantial IO performance improvements, and support for out-of-core XGBoost training.

Zero-Code-Change Acceleration

The new capabilities of NVIDIA’s cuML now allow data scientists to leverage zero-code-change acceleration in their workflows. This functionality is particularly beneficial for users of popular libraries such as scikit-learn, UMAP, and hdbscan. By utilizing Nvidia GPUs, data scientists can achieve performance gains of 5-175x without altering their existing codebases.

IO Performance Enhancements

RAPIDS’ cuDF has received significant performance boosts, particularly for cloud-based data processing tasks. The integration of NVIDIA KvikIO enables faster reading of Parquet files from cloud storage solutions like Amazon S3, achieving a threefold improvement in read speeds. Furthermore, the hardware-based decompression engine in NVIDIA’s Blackwell architecture facilitates faster data processing by reducing latency and increasing throughput.

Out-of-Core XGBoost Training

In collaboration with the DMLC community, RAPIDS has optimized XGBoost for large datasets, allowing for efficient training on data exceeding in-memory limits. This development is especially advantageous for systems utilizing NVIDIA’s GH200 Grace Hopper and GB200 Grace Blackwell, enabling them to handle datasets over 1 TB efficiently.

Usability and Platform Updates

RAPIDS has also enhanced usability with features like global configuration settings and GPU-aware profiling for the Polars engine, making it easier for users to optimize their data science workflows. Additionally, support for NVIDIA Blackwell-architecture GPUs and improvements in Conda package management have been introduced, broadening the platform’s accessibility and ease of use.

These updates, showcased at NVIDIA GTC 2025, underline NVIDIA’s commitment to advancing data science technology and streamlining machine learning processes. For more detailed information on these developments, visit the NVIDIA blog.

• nvidia
• rapids
• machine learning
• xgboost