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Home»ADOPTION NEWS»NVIDIA Rapids improve machine learning with zero code acceleration and performance gain.
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NVIDIA Rapids improve machine learning with zero code acceleration and performance gain.

By Crypto FlexsJune 2, 20252 Mins Read
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NVIDIA Rapids improve machine learning with zero code acceleration and performance gain.
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Timothy Morano
May 31, 2025 05:45

NVIDIA’s Rapids introduces zero code acceleration for machine learning, improves IO performance, and simplifies data science workflows by supporting core XGBOOST training.





NVIDIA has announced significant developments in the Rapids software product line, which focuses on accelerating machine learning and improving performance. According to NVIDIA, the latest update introduces a zero code change acceleration for Python Machine Learning, significant IO performance improvement and core and other XGBOOST education support.

Zero code change acceleration

NVIDIA’s new features allows data scientists to take advantage of zero code change in workflow. This feature is especially useful for popular library users such as Scikit-Learn, Umap and HDBSCAN. By using the NVIDIA GPU, data scientists can achieve performance gains of 5-175X without changing the existing codebase.

IO performance improvement

Rapids’ CUDF has received significant performance improvements in cloud -based data processing. The integration of NVIDIA KVIKIO allows you to read the parquiz file of cloud storage solutions such as Amazon S3 faster, enhancing the read speed tripled. In addition, NVIDIA’s BLACKWELL Architecture’s hardware -based reduced pressure engine reduces the waiting time and increases the amount of throughput to facilitate more data processing.

Core and other XGBOOST training

In cooperation with the DMLC community, RAPIDS optimized XGBOOST for large data sets to provide efficient training for data that exceeds the limitations of memory. This development is especially advantageous for a system using NVIDIA’s GH200 Grace Hopper and GB200 Grace Blackwell, and can efficiently process data sets of 1 TB or more.

Useful and platform update

Rapids also enhance usefulness with the same features such as the Global Configuration setting and the GPU recognition profile ring of the Polars Engine, allowing users to optimize data science work more easily. In addition, support for NVIDIA Blackwell-Ararchitecture GPU and improvement in Conda package management have been introduced, expanding the platform’s accessibility and convenience.

These updates exhibited in NVIDIA GTC 2025 emphasize NVIDIA’s promises to simplify data science and technology development and machine learning processes. For more information about this development, visit the NVIDIA blog.

Image Source: Shutter Stock


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