As AI computing expands across hybrid and multi-cloud environments, infrastructure teams are under pressure to accelerate time-to-insight while maximizing GPU investments. But too often, storage becomes the bottleneck.
Whether you’re training foundation models or deploying agentic AI applications, one thing is clear: GPU compute cycles are precious—and they’re getting harder to fully utilize. During training, checkpointing can stall progress while data is written to slow network storage. During inferencing, even millisecond latencies can degrade user experiences and drive-up costs.
Indeed, as Krista Case and Camberley Bates of The Futurum Group note, “With growing AI model complexity and larger datasets, delayed data retrieval forces GPUs to remain idle instead of performing computations. Given the significant investment in GPU infrastructure, enterprises are now prioritizing solutions that eliminate storage inefficiencies, ensuring seamless data flow to maximize processing power and cost efficiency.”
Enter Hammerspace Tier 0: a solution that turns the local NVMe storage within a cluster of GPU servers into a new tier of lightning-fast shared storage, managed and protected by Hammerspace. It can be activated in hours – no forklift upgrades, no complex integrations, just instant access to fast, shared storage that keeps pace with your GPUs.
Tier 0 delivers up to 10x the performance of traditional network storage – on-prem or in the cloud – so you can reduce checkpointing time, increase GPU utilization, and improve response times for inferencing and agentic AI. And because Tier 0 is just another tier within the Hammerspace Data Platform, moving data between on-prem storage systems and cloud compute clusters is a snap.
Finally, by letting you use the NVMe capacity you already own, Tier 0 eliminates the need for additional storage systems, saving power, space, and budget. In large GPU clusters, the savings can add up to millions of dollars.
In Summary
Hammerspace Tier 0 is by far the most performant shared storage type currently available for AI workloads, with benchmarks demonstrating Tier 0 can support 16.5x more GPUs than a Lustre solution with an equivalent number of client connections. And when this capacity is aggregated across multiple GPU servers in a cluster, organizations can get far greater output from their GPU investments at significantly lower costs, both on-premises and in the cloud.
More importantly, this innovation works with existing on-premises and/or cloud-based GPU (or CPU/ARM) servers from any vendor, by activating internal local NVMe capacity that has already been paid for. When activated, Tier 0 is by far the most performant storage type currently available for AI workloads. And when this capacity is aggregated across multiple GPU servers in a cluster, organizations can get far greater output from their GPU investments at significantly lower costs, both on-premises and in the cloud.
This bears repeating: This ultra-fast local NVMe capacity – which is largely underutilized – is a sunk cost, since it is already included within the price of GPU servers in both on-premises and cloud-based deployments. The fact that this also doesn’t add any additional power requirements is also a net benefit for data centers who are near the limit of their available power.
Learn more in our educational webinar as Sam Newnam, Senior Director of AI Solutions, and Eric Bassier, Senior Director of Product Marketing, discuss how Hammerspace Tier 0 radically simplifies the path to AI-ready infrastructure. Register today!
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