AI Ready Storage is data infrastructure designed to break down silos and give enterprises seamless, high-performance access to their data wherever it lives. With 73% of enterprise data trapped in silos and 87% of AI projects failing to reach production, the bottleneck isn’t GPUs—it’s data. Traditional environments suffer from visualization challenges, high costs, and data gravity that limits AI flexibility. Hammerspace simplifies the enterprise data estate by unifying silos into a single global namespace and providing instant access to data—without forklift upgrades—so organizations can accelerate AI success.

The presentation focused on leveraging existing infrastructure and data to make it AI-ready, emphasizing simplicity for AI researchers under pressure to deliver high-quality results quickly. Hammerspace simplifies the data readiness process, enabling easy access and utilization of data within infrastructure projects. While the presentation covers technical aspects, the emphasis remains on ease of deployment, workload management, and rapid time to results, aligning with customer priorities. Hammerspace provides a virtual data layer across existing infrastructure, creating a unified data namespace enabling access and mobilization of data across different storage systems, enriching metadata for AI workloads, and facilitating data sharing in collaborative environments.

Hammerspace addresses key AI use cases such as global collaboration, model training, and inferencing, particularly focusing on enterprise customers with existing data infrastructure they wish to leverage. The platform's ability to assimilate metadata from diverse storage systems into a unified control plane allows for a single interface to data, managed through Hammerspace for I/O control and quality of service. By overcoming data gravity through intelligent data movement and leveraging Linux advancements, Hammerspace enables data access regardless of location, maximizing GPU utilization and reducing costs. This is achieved by focusing on data access, compliance, and governance, ensuring that AI projects align with business objectives and minimizing risks associated with data movement.

Hammerspace aims to unify diverse data sources, from edge data to existing storage systems, enabling seamless access for AI factories and competitive advantages through faster data insights. With enriched metadata and automated workflows, HammerSpace accelerates time to insight and removes manual processes. HammerSpace is available as installable software or as a hardware appliance, and supports various deployment models, offering linear scalability and distributed access to data. A "Tier 0" capability was also discussed, which leverages existing underutilized NVMe storage within GPU nodes to create a fast, low-latency storage pool, showcasing the platform's flexibility and resourcefulness.