As organizations struggle with fragmented data estates that stall AI initiatives before they deliver value, Hammerspace arrives at SC25 (Booth #3523) with a clear message: the bottleneck isn’t GPUs or algorithms—it’s data chaos. The company’s hybrid cloud data platform addresses the fundamental challenge facing HPC and AI organizations across research, government, and enterprise sectors by unifying data silos into a single global namespace and orchestrating automated data delivery to GPUs and AI models. With a standards-based approach rooted in deep technical expertise from the Linux, NFS, and open-source communities, Hammerspace enables teams to make their data AI-ready without costly re-platforming or vendor lock-in.
At this year’s show, Hammerspace will showcase breakthrough innovations including their Tier 0 solution, which transforms local NVMe storage within GPU clusters into high-performance shared storage that’s up to 10x faster than networked alternatives while reducing power requirements by approximately 95%. SVP of Global Marketing Molly Presley sits down with VMblog to discuss how the company’s AI Data Platform helps organizations achieve better price-performance ratios in an era of skyrocketing AI training costs, eliminates the friction between data silos and compute, and positions enterprises for the coming paradigm shift where data orchestration becomes more critical than GPU availability. Attendees can also catch live podcast recordings, partner demonstrations from QStar Technologies and ScaleFlux, and enter daily raffles for prizes including Meta Ray-Ban Glasses and AirPods 4.
VMblog: If you were giving an SC25 attendee a quick overview of your company, what would you say? How would you describe your company’s evolution since SC24?
Molly Presley: Hammerspace is the data platform to accelerate AI anywhere -on the infrastructure you already own. The Hammerspace hybrid cloud data plane unifies data silos into a single global namespace and orchestrates automated data delivery to GPUs and AI models.With metadata‑indexed discovery and a standards‑based approach, teams can find and use the right data fast while maintaining data control and governance. Built to work across sites, clouds, and existing storage, Hammerspace helps organizations make their data AI‑ready without re-platforming, eliminate lock‑in, and accelerate AI initiatives.
The company was founded with a mission to make data an instantly accessible globally shared resource, no longer trapped within storage silos. Our founder and CEO, David Flynn, was previously co-founder and CEO of Fusion.io, a pioneer in flash storage technology.
Hammerspace has deep technical roots in the Linux, NFS, and open-source communities, and has made extensive contributions to the Linux NFS kernel, including work that has pioneered a standards-based parallel file system architecture now deployed in some of the largest GPU computing environments in the world.
VMblog: What key message will SC25 attendees hear from you this year? What concrete value proposition will they take back to convince their C-suite and budget holders in today’s tighter economic climate?
Presley: At SC25, Hammerspace will demonstrate how to simplify your data pipeline and accelerate enterprise AI workflows by transforming all of your data on existing storage into a single, unified resource for any application, any GPU, on any infrastructure.
- Unify access and control of all data across any storage without the cost, risk, and disruption of data migrations. Hammerspace unifies your data estate by creating a single multi-protocol global namespace that spans existing and new storage across all locations. Your files and objects-whether on-premises, in the cloud, or at the edge-become globally accessible via standard file/object protocols, with no need for proprietary client software or agents. This enables seamless collaboration and streamlines distributed workloads for AI and other use cases, without needing to migrate data to new repositories.
- Apply global business rules across all silos to meet performance and cost requirements. Hammerspace automates control of distributed data with an objectives-based policy engine that manages data protection, data placement, and other critical data services. Powered by metadata-driven intelligence, Hammerspace Service Level Objectives reduce the complexity of wrangling multi-silo, multi-site environments, ensuring your data is where it needs to be to meet performance, protection, and budget requirements.
- Deliver data to high-performance workloads without disrupting users or applications. Hammerspace transparently orchestrates unstructured data across silos and locations in the background-without disrupting users or applications, even on live data. Triggered by Objectives from the Hammerspace automation engine, data placement is seamless across all storage types from any vendor up and down the performance hierarchy. Feed GPUs or other high-performance workloads across on-premises, remote, or clouds without the disruption or vendor lock-in of traditional data tiering or migration point solutions.
- Open, high-performance architecture that bridges all storage types. Leverage data in place across any storage, from any vendor, for any use case, while activating our standards-based parallel file system to accelerate AI workloads on existing or new infrastructure. This includes transforming local NVMe within on-premises or cloud GPU clusters into a high-performance shared storage tier, delivering performance that surpasses any networked storage in the industry-and without needing proprietary client software.
VMblog: Given the current challenges facing HPC organizations-from budget constraints to talent shortages-what specific problems does your technology solve for SC25 attendees?
Presley: HPC and AI organizations across research, government and enterprise, all face the same challenge: how to turn fragmented, distributed data into a unified foundation for AI. Everyone wants to build generative, agentic, and domain-specific AI – but most projects stall long before they deliver value.
The problem isn’t GPUs or algorithms. It’s data chaos. Fragmented, duplicated, and trapped across storage silos, clouds, and business units, combined with a shortage of specialized talent to manage the growing sprawl of AI data pipelines.
Hammerspace eliminates the friction between data silos and compute, giving organizations the power to innovate faster, anywhere their data lives. Our Data Platform unified data foundation that enterprises need to make AI successful, anywhere.
At the core of Hammerspace’s AI Data Platform is a global namespace. This is a live, unified view of all unstructured enterprise data across storage types and locations. Using data assimilation, millions of files across clouds and data centers become instantly accessible, without moving a byte.
- Open, standards-based protocols(NFS, SMB, S3, pNFS) deliver high-performance data to the fastest GPUs without proprietary lock-in.
- Our Tier-0 NVMe architectureintegrates local GPU storage into a shared, ultra-fast pool, turning every node into a high-performance contributor.
- Model Context Protocol (MCP) integrationlinks business data directly to AI agents for retrieval-augmented reasoning.
- Embedded vector databasetransforms files into searchable embeddings for contextual, real-time access across your global data estate.
Why It Matters
- Accelerate AI time-to-value- Deliver production-ready data in weeks, not months.
- Reduce infrastructure waste- Use what you have; scale only when needed.
- Simplify operations- One platform, one namespace, zero silos.
- Empower teams- Free scarce data engineers to focus on innovation, not integration.
VMblog: How can attendees find you at SC25? What hands-on demonstrations, live benchmarks, or interactive experiences will you have at your booth this year?
Presley: SC attendees can find Hammerspace in booth #3523, where our top executives will be there along with technical experts to brief attendees on our solutions for HPC and AI storage, and hybrid-cloud data orchestration. We will demonstrate our AI Data Platform as well as our Tier 0 solution, which transforms the local NVMe storage within GPU clusters into a shared, high-performance storage tier.
Additionally, several key partners will be exhibiting in our booth including:
- QStar Technologies – Demonstrating how its QStar Archive Manager and Global ArchiveSpace software simplify large-scale HPC and AI archiving using Qualstar Q Series tape libraries.
- ScaleFlux – Highlighting how its computational storage and memory technologies redefine the performance and efficiency of data infrastructure from core to edge.
We will be conducting live podcast interviews with industry thought leaders in our booth for the Data Unchained podcast with host Molly Presley.
VMblog: What keeps bringing you back to SC year after year?
Presley: We have sponsored SC25 for the past few years and keep coming back because it provides a direct connection to our future and current client base.
VMblog: Do you have any speaking sessions, panel discussions, or Birds of a Feather meetings during SC25? How can attendees join? What topics will you be addressing?
Presley: Yes! Floyd Christofferson, Vice President of Product Marketing at Hammerspace, will present in a featured session titled “Open Flash Platform: An Initiative for Open, Highly Efficient, Exascale AI Storage” on November 20 (2:30 p.m. – 3:00 p.m., Room: 130)
The session will explain the vision and engineering that make the Open Flash Platform (OFP) possible, demonstrating how open, standards-based architectures can simplify and reduce the costs of high-capacity storage.
VMblog: As AI training costs have skyrocketed and organizations scrutinize ROI more carefully, how are your solutions helping customers achieve better price-performance ratios while maintaining competitive AI capabilities?
Presley: AI workloads are resource-intensive, increasing power usage and operational costs. They require high-throughput, low-latency storage solutions that can rapidly feed data to GPUs to keep them busy, ensuring efficient training and inference workloads while containing costs.
Solutions like NVMe solid-state drives (SSDs) and power-aware storage management reduce energy consumption without compromising performance. The challenge is to optimize the placement of data to this power-efficient tier while keeping the bulk of data in low-cost or cloud-based storage types, and to do so without creating new silos or interrupting access to data. This is where Hammerspace’s AI Data Platform can help organizations solve the problem holistically, leveraging data intelligence to ensure resources are optimized globally across all silos and locations.
Additionally, AI projects require high-performance storage that scales linearly in both capacity and performance. Our Hyperscale NAS architecture has proven the ability to not only scale linearly from small environments to hyperscale AI use cases using any commodity storage, but also to accelerate existing storage infrastructure to keep up with the demands of GPU computing. It helps you get more out of your existing storage investments and improve ROI as you embark on your organization’s AI journey.
External NVMe storage arrays provide the performance necessary for AI workloads to a point. But Hammerspace’s Tier 0 capabilities activate another class of NVMe capacity that is largely unutilized within the GPU servers themselves. Hammerspace’s Data Platform uniquely unlocks NVMe capacity within existing servers to create a new protected Tier 0 that dramatically accelerates performance even compared with the fastest external NVMe arrays available.
This capability drastically reduces checkpoint time, resulting in better utilization of your GPUs and reduced costs. Unlike hyperconverged systems, Hammerspace Tier 0 does this without creating an isolated silo, and while leveraging sunk costs within infrastructure that is already in place. The powerful Tier 0 capability unlocks this valuable resource to create a seamless, protected class of extreme-performance storage within the infrastructure you already own.
Our high-performance AI Data Platform accelerates model training and inference without requiring you to abandon your existing storage investments. Organizations can drive AI efficiency by leveraging scalable, high-throughput, and intelligently managed storage without creating new storage silos or adding complexity to IT staff.
With Hammerspace’s AI-optimized storage architectures, plus its global data management and intelligent data orchestration capabilities, IT leaders and data scientists can unlock the full potential of their AI initiatives while keeping costs under control.
VMblog: Power efficiency has become critical with data centers hitting energy limits. What breakthrough technologies are you showcasing at SC25 to deliver superior performance per watt, and what real-world efficiency gains can customers expect?
Presley: At SC25, Hammerspace will be demonstrating our Tier 0 solution, which turns GPU server-local NVMe into high-performance shared storage, managed and protected by Hammerspace. Tier 0 storage is up to 10x faster than networked storage – so you can reduce checkpointing time for AI training and HPC and improve response times for inferencing and agentic AI.
With Tier 0, organizations can fully leverage the local NVMe on GPU servers as a high-performance shared resource with the same redundancy and protection as traditional external storage. This integration makes the server local NVMe capacity part of a unified parallel global file system, seamlessly spanning all storage types with automated data orchestration for greater efficiency and flexibility.
Utilizing this otherwise unused high-performance storage, Tier 0 reduces the amount of expensive external storage systems organizations need to buy, slashes power requirements and reclaims valuable data center space. This breakthrough leverages the already sunk investment and deployed capacity in existing NVMe storage within the GPU servers, maximizing utilization and accelerating time to value.
With Tier 0, Hammerspace turns isolated NVMe into a high-performance shared resource, reducing power demands for that storage capacity by approximately 95% compared with deploying external networking and storage. Accessible via a global, multi-protocol namespace, it spans silos, sites, and clouds, enabling seamless data access for users and applications. This integration keeps active datasets local to GPU servers, allows AI model checkpoints to be written locally, and automates tiering across Tier 0, Tier 1 and Tier 2 storage.
Hammerspace Tier 0 unlocks a new era of efficiency in GPU computing. By streamlining data handling and accelerating checkpointing times up to 10x faster than external storage, Tier 0 eliminates the lag that’s been holding back GPU utilization. With every second shaved off checkpointing, GPU resources are freed up to tackle other tasks, translating to real cost savings and up to 20% more output from existing investments. In large GPU clusters, this adds up to millions in recaptured value-an essential breakthrough for any organization serious about harnessing the power of high-performance computing.
Stop by our booth and see it for yourself!
VMblog: Memory bandwidth and storage latency remain critical bottlenecks for modern AI workloads. What innovations in memory hierarchy, storage orchestration, or data movement technologies will you be unveiling at SC25?
Presley: As organizations move AI workloads to the cloud, managing the flow of data between on-premises and cloud environments becomes a significant challenge. AI models rely on large volumes of data, often stored in various silos across multiple vendor platforms and locations. When the choice is to make copies of entire datasets to push to the cloud or to another net-new bespoke storage silo, the ROI of such projects plummets, with added cost, IT complexity, and risk.
Hammerspace’s data orchestration unifies data that is siloed in disparate storage systems, then uses no-code business rules to automate data management and protection. Files are distributed across storage systems, both on-premises and across different clouds and cloud regions, so data can be brought to the compute resources, applications, and users that need it, when they need it.
One of the key innovations of Hammerspace’s Data Platform is its ability to decouple the high-performance file system from the underlying storage infrastructure to enable this orchestration to become seamless in the background. This allows data to remain in place-whether on-premises or in the cloud-while still being accessible at high performance across distributed environments.
By bridging incompatible storage silos, Hammerspace eliminates the need to move massive datasets into the cloud just to process AI workloads. Instead, the system automates the orchestration of data at a file-granular level to remote compute resources as needed, allowing AI models to access the data directly, wherever it resides.
This capability ensures that high-performance AI applications-such as deep learning model training, inferencing, and analytics-can be fed with the right data at the right time, whether that data is stored on-premises, in the cloud, or across multiple geographic locations. And that this can be done automatically, and without the need to migrate whole data sets.
As HPC and research organizations scale their AI initiatives, the ability to seamlessly orchestrate data will be essential to overcoming the bottlenecks that often accompany AI scaling efforts.
VMblog: Looking toward 2026 and beyond, what transformative trends-from neuromorphic computing to digital twins of entire ecosystems-do you see reshaping supercomputing, and how is your company positioning for these paradigm shifts?
Presley: We predict the end of data fragmentation as AI’s silent killer. In 2026, enterprises will need to confront fragmented data estates. The industry will recognize that the biggest limiter to AI adoption isn’t GPU supply-it’s data access speed, consistency, and reach. Organizations will shift investment from more compute to unified data platforms that make existing infrastructure AI-ready.
By the end of 2026, AI deployments will rely on data orchestration layers that abstract away underlying storage silos and present a single, global view of data across hybrid environments. This approach will mark the beginning of the post-storage era-where AI agents, RAG workflows, and LLMs access information anywhere it resides, without copying or migrating it.
The winners of the AI race will be those who treat data fragmentation not as a symptom to be managed, but as a core architectural flaw to be eliminated. Performance, cost efficiency, and scalability will all flow from this unification-turning “AI Anywhere” from an aspiration into the new enterprise standard.
By 2026, organizations will increasingly pivot from relying on commercial APIs to deploying AI workloads on-premises. Security, compliance, and governance concerns will drive demand for AI environments built on enterprise infrastructure rather than public APIs. This shift ensures organizations retain complete control of their data, models, and intellectual property – a priority as generative AI moves deeper into regulated and mission-critical use cases.
The era of focusing solely on GPU availability is coming to an end. The real competitive advantage lies in creating unified, global data estates that can power inference and generative AI at scale. Enterprises will realize that fast storage isn’t enough – orchestrating massive, decentralized, unstructured data into a single global namespace is now essential. In 2026, infrastructure players who can eliminate silos across sites, storage systems, and clouds will become the most strategic players in AI adoption.
The sheer scale of inference and GenAI workloads will force a reckoning with power and efficiency. By 2026, new infrastructure technologies – from smarter data orchestration layers to energy-aware storage and compute systems – will emerge as enterprises seek to manage costs and sustainability pressures. We expect infrastructure vendors to compete not only on speed and scale, but also on their ability to tame energy consumption while maintaining enterprise-class performance.
2026 will be remembered as the year AI moved from experimentation to industrialization – the dawn of the AI Factory. Across industries, organizations will shift their focus from simply training bigger models to operationalizing intelligence at scale. The frontier will no longer be just about model size, but about how efficiently those models are fed, reasoned with, and deployed.
The world’s compute capacity is now bounded by energy and data movement, not transistors. As a result, efficiency will become the new scoreboard of AI progress – measured in tokens-per-watt, throughput-per-rack, and time-to-insight. Enterprises will realize that GPUs sitting idle due to data fragmentation or latency are not just a technical problem, but an economic one.
VMblog: What prizes, exclusive demos, or special giveaways will you have at your SC25 booth to engage with attendees?
Presley: Hammerspace will be hosting a daily raffle at our booth (#3523), where visitors can win fantastic prizes including Meta Ray-Ban Glasses, AirPods 4 or a $50 Amazon gift card. Attendees wanting to enter the raffle will receive a card and a stamp when they visit our AI Data Platform and Tier 0 demo stations. Come back every day to win!
In addition, we will have an assortment of the latest Hammerspace swag, including fashionable t-shirts, socks and hats, pens, mobile chargers, and collectible figurines of our beloved Hammerspace superhero, Data Dynamo!
