Snowflake Supercharges Machine Learning for Enterprises with Native Integration of NVIDIA CUDA-X Libraries
Snowflake ML and NVIDIA integration puts high-speed AI development in the hands of data scientists
- Collaboration enables NVIDIA's popular CUDA-X libraries to be seamlessly used within the Snowflake AI Data Cloud
- Integration empowers data scientists to massively accelerate AI workflows over Snowflake data using popular frameworks without any code changes
- NVIDIA's benchmarks show that certain AI workflows on NVIDIA GPUs can now run up to 200x faster than on CPUs
Snowflake (NYSE:SNOW), the AI Data Cloud company, announced a new integration with NVIDIA to accelerate ML workflows directly within Snowflake's platform. Through the integration, Snowflake ML will now come preinstalled with some of NVIDIA's most popular libraries for data science, offering Snowflake customers the ability to leverage GPU-accelerated algorithms for their ML workflows. This native integration simplifies and streamlines the entire ML model development lifecycle, allowing data scientists to accelerate model development for essential Python libraries, with no code changes required.
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Snowflake Supercharges Machine Learning for Enterprises with Native Integration of NVIDIA CUDA-X Libraries
"Our vision is to help every company leverage data and AI with ease, security and performance, and this collaboration with NVIDIA helps us advance that goal," said Christian Kleinerman, EVP of Product, Snowflake. "By natively integrating NVIDIA CUDA-X libraries, we're giving our customers a massive performance boost. This isn't just about faster performance; it's about enabling our data scientists to spend less time on infrastructure and more time deriving insights and achieving strong business outcomes for their organizations."
As enterprise datasets grow to unprecedented sizes, the need for GPU acceleration has become critical to maintaining productivity and managing costs. NVIDIA's benchmark runs show speed up of 5x for Random Forest and up to 200x for HDBSCAN on NVIDIA A10 GPUs compared to CPUs. Through this integration, NVIDIA cuML and NVIDIA cuDF libraries – part of the NVIDIA CUDA-X Data Science (CUDA-X DS) ecosystem – are available in Snowflake ML to accelerate development cycles for scikit-learn, pandas, UMAP and HDBSCAN, without the need for code changes.
"Data is the raw material of intelligence, and transforming it into insight is the foundation of generative and agentic AI," said Pat Lee, VP of Strategic Enterprise Partnerships, NVIDIA. "By integrating NVIDIA cuDF and cuML libraries directly into the Snowflake ML platform, customers can now harness accelerated computing with their existing Python workflows, eliminating complexity and dramatically speeding up AI development."
The integration makes NVIDIA's powerful CUDA-X Data Science (CUDA-X DS) ecosystem, an open-source suite of GPU-accelerated libraries, accessible directly through the Snowflake Container Runtime, a pre-built environment for large-scale machine learning development. Organizations now have the power to tackle computationally demanding challenges, such as:
- Large-Scale Topic Modeling: Reducing the time to process and cluster massive tabular datasets—such as millions of product reviews—from hours on a CPU to minutes on a GPU.
- Computational Genomics Workflows: Significantly speeding up the analysis of vast, high-dimensional sequences, allowing researchers to rapidly perform classification tasks (e.g., predicting gene families) and focus on insights rather than low level GPU computing.
The integration builds on Snowflake and NVIDIA's continued collaboration to power generative AI capabilities within the AI Data Cloud. This new step further solidifies Snowflake's commitment to deliver cutting-edge performance for all phases of the data and AI lifecycle. The companies will continue to work closely to provide Snowflake customers with seamless access to some of the most advanced GPU-accelerated tools, from traditional ML model development to the deployment of enterprise-grade LLMs.
Joint customers can access this new capability today through the Container Runtime in Snowflake Notebooks or via remote execution facilitated by ML Jobs.
Learn More:
- For more information on the Snowflake and NVIDIA partnership read this blog post.
- Stay on top of the latest news and announcements from Snowflake on LinkedIn and X.
About Snowflake
Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 12,000 customers around the globe, including hundreds of the world's largest companies, use Snowflake's AI Data Cloud to build, use and share data, applications and AI. With Snowflake, data and AI are transformative for everyone. Learn more at snowflake.com (NYSE:SNOW).
Forward Looking Statements
This press release contains express and implied forward-looking statements, including statements regarding (i) Snowflake's business strategy, (ii) Snowflake's products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake's products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading "Risk Factors" and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events.
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Partner PR, Snowflake
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