Nvidia and DataStax just made generative AI smarter and more economical — here’s how
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Nvidia and DataStax launched today a new technology that dramatically reduces storage requirements for companies deploying generative AI systems while enabling faster and more accurate information retrieval in multiple languages.
The new one Nvidia NeMo Retriever microservicesintegrated with DataStax’s AI platformreduces data storage volume by 35x compared to traditional approaches – a crucial opportunity as enterprise data is expected to reach more than 20 zettabytes by 2027.
“Today’s enterprise unstructured data is 11 zettabytes, which equates to roughly 800,000 Library of Congress copies, 83% of which is unstructured, with 50% being audio and video,” said Carrie Briskey, vice president of product management for AI at Nvidia, in an interview with VentureBeat. “Significantly reducing these storage costs while allowing companies to efficiently embed and retrieve information is becoming a game changer.”
Technology is already proving transformative for Wikimedia Foundationwhich used the integrated solution to reduce processing time for 10 million Wikipedia entries from 30 days to under three days. The system handles real-time updates to hundreds of thousands of records that are edited daily by 24,000 global volunteers.
“You can’t just rely on big content language models—you need context from your existing enterprise data,” explained Chet Kapur, CEO of DataStax. “That’s where our hybrid search capability comes in, combining both semantic search and traditional text search, then using Nvidia’s re-ranking technology to deliver the most relevant results in real-time on a global scale.”
Enterprise data security meets AI accessibility
The partnership addresses a critical challenge facing enterprises: how to make their vast stores of personal data accessible to AI systems without exposing the sensitive information to external language models.
“Take FedEx – 60% of their data is in our products, including all package delivery information for the last 20 years with personal data. That’s not going to get to Gemini or OpenAI anytime soon, or ever,” Kapur explained.
The technology is finding early adoption across industries, with financial services firms leading the way despite regulatory restrictions. “I was blown away by how far ahead financial services firms are now,” Kapur was quoted as saying Commonwealth Bank of Australia and Capital one as examples.
The Next Frontier for AI: Multimodal Document Processing
Looking ahead, Nvidia plans to expand the technology’s capabilities to handle more complex document formats. “We’re seeing great results with multimodal PDF processing—understanding tables, graphs, charts, and images and how they connect between pages,” revealed Briskey. “It’s a really tough problem that we’re excited to tackle.”
For enterprises drowning in unstructured data while trying to implement AI responsibly, the new offering provides a way to make their information assets AI-ready without compromising security or breaking the bank on storage costs. The solution is available immediately via Nvidia API Catalog with a 90-day free trial license.
The announcement underscores the growing focus on enterprise AI infrastructure as companies move beyond experimentation to large-scale deployment, with data management and cost efficiency becoming critical success factors.
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