Nvidia and DataStax have made generative AI smarter and leaner — here’s how

Nvidia and DataStax Today it launched new technology that dramatically reduces storage requirements for companies deploying generative AI systems, while enabling faster and more accurate information retrieval across multiple languages.

New Microservices for Nvidia NeMo Retrieverintegrated with DataStax’s AI platformreduces data storage size by 35 times compared to traditional methods – a critical capability, as enterprise data is expected to More than 20 zettabytes by 2027.

“Today’s enterprise unstructured data volume is 11 zettabytes, the equivalent of approximately 800,000 copies of the Library of Congress, of which 83% is unstructured, and 50% is audio and video,” said Cary Brisky, vice president of AI Product Management at Nvidia. In an interview with VentureBeat. “Dramatically reducing these storage costs while enabling companies to efficiently embed and retrieve information becomes a game-changer.”

Technology has already proven to be transformative for Wikimedia Foundationwhich used the integrated solution to reduce the processing time for 10 million Wikipedia entries from 30 days to less than three days. The system handles real-time updates across hundreds of thousands of entries edited daily by 24,000 global volunteers.

“You can’t just rely on big language models of content — you need context from your existing organization’s data,” explains Chet Kapoor, CEO of DataStax. “This is where our hybrid search capability comes in, combining semantic search with traditional text search, then using Nvidia’s reranking technology to deliver the most relevant results in real-time on a global scale.”

Enterprise data security matches accessibility to AI

The partnership addresses a major challenge facing companies: how to make their vast stores of private data accessible to AI systems without exposing sensitive information to external language models.

“Take FedEx for example – 60% of their data is in our products, including all the package delivery information over the last 20 years with personal details. This is not coming to Gemini or OpenAI anytime soon or ever,” Kapoor explained. It’s gone.

This technology is seeing early adoption across industries, with financial services companies leading the charge despite regulatory constraints. “I was amazed at how far financial services companies have come now,” Kapoor said. Commonwealth Bank of Australia and Capital One As examples.

The next frontier of artificial intelligence: multimedia document processing

Looking to the future, Nvidia plans to expand the technology’s capabilities to handle more complex document formats. “We’re seeing great results with multimedia PDF processing — understanding tables, graphs, charts, and images and how they relate across pages,” Brisky revealed. “It’s a really tough problem and we’re excited to tackle it.”

For organizations drowning in unstructured data while trying to deploy AI responsibly, the new offering provides a path to making their information assets AI-ready without compromising security or breaking the bank in storage costs. The solution is available immediately through Nvidia API Catalog With a 90-day free trial license.

This announcement underscores the growing focus on enterprise AI infrastructure as companies move from pilot to large-scale deployment, with data management and cost efficiency becoming critical success factors.

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