Hammerspace operates as a non-proprietary, standards-based, software-defined data platform designed to unify an enterprise’s unstructured data estate, encompassing object storage and NAS, without requiring data movement. It orchestrates and automates data movement based on policies, enabling acceleration to GPU processors via its PNFS parallel file system, which leverages standard NFS for easy integration without custom clients. The presentation highlighted that while enterprises need comprehensive visibility into their data for AI, traditional centralization methods are no longer viable due to the sheer volume and distribution of data. Current challenges for scaling AI applications include severe constraints on power, compute (GPUs), and even storage components like SSDs and disk drives, with cloud providers also facing capacity limitations. These realities mean simply buying more hardware is often not an option, exposing the limitations of a centralized, siloed data approach.
Hammerspace addresses these constraints by maximizing and optimizing existing resources. This includes unifying stranded capacity within distributed NAS infrastructures and incorporating local NVMe storage (termed “Tier Zero”) on AI servers into a global namespace for high-performance, low-latency access. It modernizes current commodity storage by enabling a parallel file system to run on top, accelerating data to GPU clusters while continuing to utilize existing hardware. Several customer case studies illustrate this approach: a semiconductor company achieved better performance for LLM development by leveraging existing storage with PNFS; another customer complemented their HPC storage by utilizing Tier Zero for high-performance local storage, saving costs; and a company shifted from NAS to object storage for data scientists, extending their namespace to GCP for additional GPU capacity, all transparently. Hammerspace also supports multi-datacenter and multi-cloud strategies, allowing customers to dynamically scale GPU workloads across Azure, Nebul, and other neo-clouds, abstracting the underlying infrastructure from users.
The core message is that growth in AI is a strategic imperative, but traditional resource expansion is currently unsustainable. Hammerspace enables agility by facilitating the consumption of new resources, whether on-premises or across various cloud providers, as they become available, without necessitating complete system re-architecture. It also integrates data sovereignty capabilities through intelligent orchestration rules, ensuring data remains within defined geographic or regulatory boundaries. While the solution is primarily aimed at large enterprises grappling with petabyte-scale, performance-intensive AI and HPC problems within sophisticated environments, Hammerspace continuously strives to simplify its interface and abstract complexity, making advanced data management more accessible for these demanding workloads. The goal is to empower organizations to continue innovating and growing their AI capabilities despite current hardware and infrastructure constraints by optimizing the data layer.
Presented by Kurt Kuckein, Sr. DIrector AI Marketing, Hammerspace. Recorded live at AI Field Day 8 in San Jose, California on May 15, 2026. Watch the entire presentation at https://techfieldday.com/appearance/h… visit https://TechFieldDay.com/event/aifd8/ or https://Hammerspace.com for more information.
