Hammerspace AI Data Platform feeds distributed data directly to GPUs for Nvidia AI environments

Hammerspace Launches AI Data Platform to Accelerate Enterprise AI Deployment

Hammerspace announced general availability of its AI Data Platform at the Nvidia Global Technology Conference this week, introducing a solution designed to eliminate a critical bottleneck in enterprise AI adoption 1

1

. The eight-year-old startup positions the platform as a turnkey solution for organizations struggling to move AI projects from pilot to production, addressing what Molly Presley, senior vice president of global marketing at Hammerspace, identifies as the primary obstacle: "having enough data or access to the data and being able to govern it properly" 1

1

. Built on Nvidia's reference architecture, the platform automates the discovery, preparation, and delivery of distributed enterprise data directly to GPUs for AI workloads without requiring large-scale data migrations or new storage infrastructure.

Eliminating Data Gravity Through Automated Orchestration

The platform tackles a persistent challenge facing enterprises: data scattered across business units, storage systems, and cloud environments. David Flynn, Hammerspace co-founder and CEO, emphasized the company's mission to overcome this fragmentation. "We don't accept that data gravity is a necessity. We can eliminate data gravity. We can allow data to transcend these silos through orchestration and a single global namespace," Flynn told CRN 2

2

. The platform creates a unified metadata layer that spans multiple storage systems, automatically locating relevant files and moving them to computing resources when required 1

1

. By leveraging data in place rather than copying it into separate environments, Hammerspace eliminates the need to purchase large amounts of new flash storage, reducing infrastructure costs while accelerating AI pipelines.

Real-Time Data Processing and GPU Optimization

The system monitors enterprise storage environments for changes and automatically prepares new files for AI inference applications within seconds. "From the second a file lands, we detect it and process it," said Sam Newnam, vice president of AI and business development at Hammerspace. "We're only talking seconds from the file creation or file change" 1

1

. This capability addresses a growing concern around limited GPU availability and efficiency. The average enterprise uses less than 30% of available capacity of costly GPUs, representing significant waste in AI investments 1

1

. The platform's ability to streamline data management and orchestration ensures GPUs receive data precisely when needed, maximizing utilization rates.

Security Integration and Strategic Partnerships

Hammerspace maintains enterprise security controls and file permissions throughout the data movement process. "We've got a lot of tooling built in to make sure that we adhere to the same security primitives that exist on original data," Newman confirmed 1

1

. The platform integrates with existing security frameworks, ensuring data governance remains intact as information flows to Nvidia AI environments. The Redwood City, California-based company recently announced a partnership with Secuvy that enhances these capabilities. Flynn explained that Secuvy "looks for data that needs to be governed like financial data, things like that, within the PDF content," allowing organizations to "use the correct data with the correct models in the correct places and manage your enterprise data security and AI security properly" 2

2

. Additionally, SK Squared, a Korean investment firm related to SK Hynix, made a strategic investment in Hammerspace, signaling confidence in the platform's approach to solving enterprise AI data challenges 2

2

. Flynn noted that Hammerspace unifies data across disparate systems and presents it uniformly, allowing data to be "processed through a vector database, preconditioned, and available to be used at an AI level with inference" .