In the race to accelerate artificial intelligence (AI) innovation, storage infrastructure performance is just as critical as computational power. AI workloads 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. Without optimized storage, even the most advanced AI systems will face bottlenecks that slow operations and drive up costs.
The problem is that unstructured data may be scattered across different storage types and locations, most of which are not optimized for AI workloads. So, organizations are faced with the challenge of accommodating storage performance requirements for AI without the cost burden of adding new high-performance repositories.
Rethinking storage architectures is essential for IT leaders and data analysts so they can achieve these goals while also containing rising storage costs, reeling in the resulting growth of power requirements, and all while maximizing the utilization of expensive GPU resources. This article explores the role of enabling AI data storage with an AI Data Platform that can leverage the storage resources you already own, and presents strategies for optimizing data environments to implement cost-effective AI use cases.
Understanding High-Performance AI Storage
A high-performance AI Data Platform is designed to support the demanding requirements of AI workloads, which involve processing massive datasets that might be distributed across multiple storage types and locations. Traditional data storage architectures are organized hierarchically, with unstructured data aging down to lower performing tiers or the cloud over time.
Unlike traditional storage, however, AI-optimized storage solutions must support:
- Linear scalability: AI projects grow rapidly, requiring flexible storage architectures that do not choke as data volumes and the number of GPUs increase.
- Low Latency: Minimizing data access delays is essential to keep GPUs fully utilized.
- High Throughput: Fast data movement between storage types and compute resources ensures optimal performance.
- Automated Data Orchestration: When data is scattered across multiple storage silos, or when GPUs are cloud-based, an AI Data Platform must provide intelligent, automated data placement that can ensure the data is where it needs to be when it needs to be there, regardless of which underlying vendor storage or location the data may be currently.
The Role of AI Storage in Model Training & Inferencing
Training AI models with unstructured data requires continuous access to large amounts of distributed data, which are typically scattered across many storage types and locations. Some organizations may use third-party training models, such as Meta’s Llama 2 & 3, which reduces this problem for training. But both conventional training and the resulting inferencing workloads must contend with these incompatible silos. Addressing this problem becomes extremely expensive unless an AI Data Platform can:
- Aggregate distributed data sources: Bridging siloed data globally from across any vendor’s storage with intelligent data orchestration eliminates the need to copy all data into a net new dedicated high-performance repository.
- Accelerate GPU-compute workloads: Intelligently staging only the data you need for training/inferencing into high-performance, low latency optimized storage that is tuned for GPU workloads keeps those GPUs busy, and minimizes duplication of storage and data..
- Enhanced Experimentation: With global visibility to all data across all silos and locations, data scientists can process larger data sets and refine models more quickly without copying distributed data into net-new repositories.
- Optimized Workflows: Intelligent data orchestration ensures that high-priority datasets are staged properly, and in time, without wasting or duplicating resources.
Challenges in Storage Management for AI Workloads
Despite its importance, managing storage for AI use cases presents key challenges:
Escalating Storage Costs
With AI data sets growing at unprecedented rates, organizations must balance both cost and performance. An AI Data Platform utilizes intelligent data orchestration to automatically place selected subsets of data from any lower cost or remote storage tier directly to the high-performance storage close to compute resources, whether on-premises or in the cloud. Unlike legacy HSMs (hierarchical storage management platforms) that only rely on file age to demote data down the hierarchy, an AI Data Platform uses metadata intelligence to directly and non-disruptively place data from any source to any location, which dramatically reduces costs and eliminates duplicated resources.
Excessive Power Consumption
AI workloads are resource-intensive, increasing power usage and operational 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 an AI Data Platform can help organizations solve the problem holistically, leveraging data intelligence to ensure resources are optimized globally across all silos and locations.
GPU Utilization Bottlenecks
When storage lags, GPUs sit idle, leading to inefficiencies. Ensuring that data is intelligently placed so that GPUs are fully utilized reduces costs, but also accelerates AI workloads.
Strategies for Optimizing Your Storage for AI Use Cases
Invest in Scalable Storage Solutions
AI projects require high-performance storage that scales linearly in both capacity and performance. Hammerspace’s 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.
Leverage Extreme Performance Without Adding Extreme Costs
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 Global Data Platform software 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.
Implement Intelligent Data Management
An AI Data Platform enables organizations to benefit from a global view across any storage type from any vendor or location, with automated data placement to move only those files needed for AI workloads. Hammerspace’s Global Data Platform software orchestrates data placement of only the files that are needed, from wherever they are today to wherever they are needed for high-performance AI workloads. This also includes orchestrating data to cloud-based or remote GPU resources without needing to copy all your data there. This non-disruptive data orchestration eliminates duplication of resources, and contains costs for high-performance AI use cases.
Optimize Storage for GPU Workloads
Aligning storage performance with GPU processing capabilities requires minimizing data transfer bottlenecks and latency. But containing costs also means that organizations must reduce or even avoid the unnecessary expense of purchasing net-new dedicated high-performance storage for AI use cases. Hammerspace bridges storage silos from any vendor and performance level, and across locations at the edge and in the cloud. It does this with extreme scalability, utilizing its high-performance parallel global file system to ensure that AI applications can access large-scale datasets wherever they may be without delays, and without the waste of unnecessary file copies.
Why Hammerspace is the Right Choice for AI Storage
AI storage isn’t just about performance—it’s about maximizing ROI, simplifying management, and future-proofing the infrastructure you already own. With Hammerspace, organizations benefit from:
- Increased GPU Utilization: Eliminating storage bottlenecks reduces GPU idle time.
- Cost Savings: Intelligent data placement and access to otherwise underutilized resources reduce unnecessary infrastructure spending.
- Seamless Hybrid and Cloud Integration: AI workloads span on-prem, cloud, edge, and multi-site environments—Hammerspace ensures data is accessible everywhere globally, regardless of which storage it may be on today or is moved to tomorrow.
A 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. An AI Data Platform like Hammerspace does this while reducing costs and power consumption. 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.
To learn more about how Hammerspace optimizes AI storage, visit Hammerspace AI Solutions.
