Hammerspace Report Finds GPUs Driving Innovation Beyond AI Workloads

Enterprise AI adoption may still be in its exploratory stages outside of hyperscalers, but companies are already finding unexpected value from the GPU infrastructure originally deployed for AI initiatives, according to a 2024 report from Hammerspace.

The company’s “State of the Next Data Cycle: How do you GPU?” report examined nearly 17,000 digital conversations involving roughly 200 enterprise technology leaders and executives across platforms including LinkedIn, Reddit, GitHub, Discord, and X/Twitter.

The analysis was designed to provide insight into how enterprises were discussing AI adoption, GPU utilization, and broader data infrastructure trends as organizations attempted to operationalize emerging AI technologies.

“The next wave of innovation is being driven by how companies activate their unstructured data,” said David Flynn, founder and CEO of Hammerspace. “Our research shows that the GPUs many enterprises originally purchased for AI projects are becoming the Swiss Army knife of data processing. This infrastructure is unlocking value in ways we never expected across various sectors.”

AI Reality Check: Conversations Over Implementation Indicates Exploration

One of the report’s primary findings was that enterprise AI adoption in late 2024 remained heavily weighted toward discussion and strategic positioning rather than full production deployment.

Among the findings highlighted in the report:

• Public conversations about AI had surged 383% since 2022.
• Approximately 60% of analyzed discussions focused primarily on thought leadership rather than implementation.
• Roughly one-third of discussions centered on innovation initiatives.
• Nearly 59% of innovation-related discussions focused on productivity enhancement.
• Only 18% of innovation conversations focused specifically on improving AI outcomes.
• Ethics represented approximately one-third of AI-related discussions, with more than half of those conversations centered on policy and best practices.

The report suggested that while AI enthusiasm was widespread, many enterprises were still attempting to determine practical operational models and measurable business value from AI investments.

A Not-So-Secret Weapon Showing Up in Unexpected Ways: GPUs

At the same time, the report found that GPU infrastructure investments were increasingly being utilized outside of traditional AI training workloads.

According to Hammerspace, many enterprises that invested heavily in GPU infrastructure for AI initiatives were instead deploying those systems for more familiar or operationally mature workloads, including large-scale analytics and accelerated data processing applications.

The report highlighted GPU-driven use cases spanning industries including hyperscale technology, scientific research, and media and entertainment.

Case studies cited in the report included deployments involving Meta, Los Alamos National Laboratory, and a large streaming media company using GPU infrastructure for applications ranging from video streaming optimization and large language model development to pandemic preparedness and climate modeling workloads.

“This trend underscores the critical need for flexible data orchestration in the modern enterprise,” Flynn said. “As GPUs evolve into versatile tools, companies must be able to efficiently move and process their data, regardless of where it resides or what hardware is being used. The businesses that can do this effectively will be the ones leading the charge in innovation.”

The broader conclusion of the report was that enterprise AI infrastructure was already beginning to evolve beyond narrowly defined AI training environments toward more generalized accelerated computing architectures capable of supporting a wide range of data-intensive workloads.

Download the full report: The State of the Next Data Cycle: How Do You GPU?

Download ebook: Unstructured Data Orchestration For Dummies, 2nd Hammerspace Special Edition