By Floyd Christofferson, Vice President of Product Marketing
As enterprises grapple with how to implement artificial intelligence (AI) strategies, they are also trying to figure out how to tie them into their existing storage and data management infrastructure without creating new storage silos. The problem is AI, machine learning (ML), and deep learning (DL) workloads need high-performance computing (HPC)-class storage and GPU-based computing, both of which go well beyond the performance capabilities of enterprise-class scale-out NAS storage.
According to a recent study by Hyperion Research, enterprises are increasingly looking to cloud-based, HPC-class compute and storage resources to solve their AI workload requirements. For example, the survey – with input from 105 enterprises across multiple verticals – showed that 64% of the respondents are exploring the use of cloud-based, high-performance compute resources as a way to complement their existing infrastructure. Additionally, 43% are already running production-level AI workloads in the cloud. This suggests that as AI use cases grow, so does the reliance on the cloud to provide scalable, on-demand computing power.
The problem is, many enterprises focus first on securing the compute resources needed to scale their AI initiatives, with the result that they then run into roadblocks on how best to get the data to the compute resources, especially when those resources are in the cloud or other remote locations.
That is, the ability for organizations to efficiently move and manage data across on-premises storage silos, and then seamlessly to cloud environments has become a significant problem that is driving up costs and complexity for AI projects. Additionally, for many organizations, the management of data across these distributed resources is often an afterthought — which puts a major hand-brake on realizing a quick return on the investment into AI for the enterprise.
The Cloud’s Role in AI Expansion
As AI becomes more deeply integrated into business processes, the cloud’s ability to offer scalable, flexible compute resources has made it a natural choice. Hyperion’s survey reveals a variety of AI activities in the cloud, with organizations testing, assessing, and porting AI workloads to the cloud at various stages:
• 39% of organizations are testing or assessing cloud-based AI-integrated workloads.
• 34% are procuring access to AI software via cloud platforms.
• 30% are securing access to AI hardware through cloud service providers.
• 31% have established limited AI-integrated pilots in the cloud.
These trends underscore that the cloud has become a focal point for AI initiatives, enabling enterprises to rapidly access compute and storage at the performance levels they need, but without the long procurement cycles associated with on-premises hardware. Cloud service providers offer an agile, responsive platform to meet AI demands, particularly for compute-heavy tasks like inferencing and model training.
Data Orchestration: Solving the Hidden Bottleneck
Despite the cloud’s growing role in AI workflows, one crucial aspect is often overlooked by IT planners: data orchestration. 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.
Which makes sense when seen in the context of a separate study that found that 35% of organizations view data management as a more significant inhibitor to scaling AI workloads than computing resources (26%). This is due to legacy data architectures that fail to meet the demands of modern AI, leading to poor input/output (I/O) performance and the difficulties of bridging to cloud-based or remote high-performance compute infrastructures.
Hammerspace: Bridging the Gap Between On-Premises and Cloud AI Workflows
The Hammerspace Global Data Platform software is designed to solve these challenges by enabling seamless, automated data orchestration between on-premises storage systems and cloud-based computing environments, including the ability to do so without wholesale data migrations.
One of the key innovations of Hammerspace 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.
Scaling AI Workflows Without Data Migrations
One of the standout examples of Hammerspace’s capabilities is its use by Meta, which operates one of the world’s largest hyperscale AI pipelines. Meta relies on Hammerspace to orchestrate data between 1,000 existing NVMe storage servers to feed the AI Research SuperCluster of 24,000 GPUs at a staggering 12.5TB per second. This solution allows Meta to avoid the costly and time-consuming process of moving data between storage systems, while still taking full advantage of its existing commodity storage and compute resources.
Additionally, in the recently published MLPerf Storage Benchmark results which simulates AI workloads, Hammerspace was the only vendor to run the tests entirely on cloud-based compute resources, matching or beating the results of dedicated on-prem HPC hardware configurations. Hammerspace was the only enterprise-class NAS solution that submitted results for the benchmark, leveraging its standards-based Hyperscale NAS architecture. This architecture utilizes the extreme parallel file system scalability pNFSv4.2, but is standards-based, without the need for client software, special networking, and with support for existing storage from any vendor.
For enterprises looking to scale AI workloads without overhauling their existing storage infrastructure, Hammerspace offers a way to automate data orchestration across multi-vendor siloed environments. Its ability to span both on-premises and cloud storage in a unified global namespace means that organizations can take advantage of cloud-based or remote GPU clusters without having to migrate all their data into net new repositories on premises or in the cloud.
Conclusion
As AI use cases expand, the reliance on cloud-based HPC resources will only increase. The Hyperion Research study clearly shows that organizations are actively exploring and integrating cloud capabilities to accelerate their AI journey. However, as enterprises scale their AI initiatives, the ability to seamlessly orchestrate data between on-premises storage and cloud compute environments will be essential to overcoming the bottlenecks that often accompany AI scaling efforts.
Hammerspace provides the solution to this challenge. With its ability to bridge the gap between siloed storage systems and cloud-based compute clusters, Hammerspace enables organizations to seamlessly extend their AI workflows across distributed environments—without the need for costly and inefficient data migrations. In doing so, Hammerspace empowers enterprises to unlock the full potential of their AI investments, ensuring that data is always where it needs to be, when it needs to be there.
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