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As artificial intelligence drives unprecedented demand for data processing, a Mountain View startup is offering a solution to one of AI’s least discussed but most critical challenges: moving and transforming massive datasets quickly enough to keep up.
Voltron Data, which announced a strategic partnership with Accenture today, has developed a GPU-accelerated analytics engine that could help enterprises overcome the data preparation bottleneck hampering AI initiatives. The company’s core product, Theseus, enables organizations to process petabyte-scale data using graphics processing units (GPUs) instead of traditional computer processors (CPUs).
“Everyone’s focused on the flashy new stuff that you can touch and feel, but it’s that data set foundation underneath that is going to be key,” said Michael Abbott, who leads Accenture’s banking and capital markets practice, in an exclusive interview with VentureBeat. “To make AI work, you’ve got to move data around at a speed and pace you just never had to before.”
Building for the AI tsunami: Why traditional data processing won’t cut it
The partnership comes as companies rushing to adopt generative AI are discovering their existing data infrastructure isn’t equipped to handle the volume and velocity of data required. This challenge is expected to intensify as AI agents become more prevalent in enterprise operations.
“Agents will probably write more SQL queries than humans did in a very short time horizon,” said Rodrigo Aramburu, Voltron Data’s CTO and co-founder. “If CIOs and CTOs are already saying they spend way too much on data analytics and cloud infrastructure, and the demand is about to step function higher, then we need a step function down in the cost of running those queries.”
Unlike traditional database vendors that have retrofitted GPU support onto existing systems, Voltron Data built its engine from the ground up for GPU acceleration. “What most companies have done when they’ve tried to do GPU acceleration is they’ll shoehorn GPUs onto an existing system,” Aramburu told VentureBeat. “By building from the ground up…we’re able to get 10x, 20x, 100x depending on the performance profile of a particular workload.”
From 1,400 servers to 14: Early adopters see dramatic results
The company positions Theseus as complementary to established platforms like Snowflake and Databricks, leveraging the Apache Arrow framework for efficient data movement. “It’s really an accelerator to all those databases, rather than competition,” Abbott said. “It’s still using the same SQL that was written to get the same answer, but get there a lot faster and quicker in a parallel fashion.”
Early adoption has focused on data-intensive industries like financial services, where use cases include fraud detection, risk modeling, and regulatory compliance. One large retailer reduced its server count from 1,400 CPU machines to just 14 GPU servers after implementing Theseus, according to Aramburu.
Since launching at Nvidia’s GTC conference last March, Voltron Data has secured about 14 enterprise customers, including two large government agencies. The company plans to release a “test drive” version that will allow potential customers to experiment with GPU-accelerated queries on terabyte-scale datasets.
Turning the GPU shortage into an opportunity
The current GPU shortage sparked by AI demand has been both challenging and beneficial for Voltron Data. While new deployments face hardware constraints, many enterprises have underutilized GPU infrastructure originally purchased for AI workloads that could be repurposed for data processing during idle periods.
“We actually saw it as a boon in that there’s just so many GPUs out in the market that previously weren’t there,” Aramburu noted, adding that Theseus can run effectively on older GPU generations that might otherwise be deprecated.
The technology could be particularly valuable for banks dealing with what Abbott calls “trapped data” — information locked in formats like PDFs and documents that could be valuable for AI training but is difficult to access and process at scale. “You’ve seen some of the data that Voltron would show you is potentially 90% more effective and efficient to move data using this technology than standard CPUs,” Abbott said. “That’s the power.”
As enterprises grapple with the data demands of AI, solutions that can accelerate data processing and reduce infrastructure costs are likely to become increasingly critical. The partnership with Accenture could help Voltron Data reach more organizations facing these challenges, while giving Accenture’s clients access to technology that could significantly improve their AI initiatives’ performance and efficiency.
