As artificial intelligence (AI) continues to transform industries, organizations are increasingly harnessing its power to drive innovation, enhance decision-making, and gain a competitive edge. However, the success of AI initiatives hinges on more than just sophisticated algorithms and powerful computing resources; it also depends on the underlying data infrastructure. Among the most critical components of this infrastructure is storage—the foundation upon which AI workloads are built and executed.
For IT leaders, ensuring that storage infrastructure is prepared for the AI future is a critical task. This involves understanding the unique demands AI places on storage, evaluating current systems, and implementing strategies to optimize storage for AI workloads. The importance of robust and scalable storage solutions becomes particularly evident when we examine how different industries leverage AI. For example, in healthcare, AI assists in medical imaging analysis, rapidly processing large image datasets to detect anomalies and assist diagnosis, requiring high-speed, low-latency storage systems. AI enhances customer experiences by analyzing vast amounts of transaction and behavior data to deliver personalized recommendations, necessitating scalable storage solutions to manage and process these extensive datasets efficiently.
These examples represent just a glimpse of AI’s broad applicability across various sectors. Regardless of the industry vertical and use case, the ability to store, manage, and access data effectively is crucial for AI to deliver meaningful results. In this blog, we will explore essential considerations for IT leaders as they prepare their storage systems to meet the demands of AI. By understanding the storage challenges and requirements across different sectors, IT leaders can better position their organizations to leverage AI for sustained innovation and growth.
Understanding the Unique Storage Demands of AI
AI workloads are fundamentally different from traditional enterprise workloads. They are characterized by high data intensity, requiring access to vast datasets to train machine learning models and execute complex computations. This presents several unique challenges for storage systems:
- Data Volume: AI requires massive amounts of data, often spanning petabytes or more. The data is not only vast but also diverse, including structured data, unstructured data (such as text, images, and video), and real-time streaming data.
- Data Velocity: AI workloads often involve real-time or near-real-time data processing, particularly in applications such as autonomous vehicles, predictive maintenance, and fraud detection. Storage systems must be capable of supporting high-speed data ingestion and retrieval.
- Data Variety: AI models thrive on diverse data types. Storage systems need to accommodate structured and unstructured data seamlessly, allowing for easy access and processing across different data formats.
- Performance Requirements: AI workloads demand high-performance storage with low latency and high throughput. The speed at which data can be read and written directly impacts the efficiency of AI model training and inference.
Given these unique demands, traditional storage solutions may fall short. IT leaders must evaluate their current storage technologies to ensure it can handle the rigors of AI workloads.
Key Considerations for Preparing Storage for AI
As IT leaders begin to assess their storage infrastructure in light of AI’s unique demands, several key considerations should be kept in mind. These considerations will guide the evaluation process and inform decisions on optimizing storage systems to meet the growing needs of AI workloads.
#1 Scalability
AI projects rarely stay small. A pilot model trained on a few terabytes of data quickly grows into petabytes once new features, retraining cycles, and additional teams come into play. That growth isn’t just about raw capacity; it’s also about keeping performance consistent as more users and workloads pile on. Without a storage layer that grows gracefully, organizations risk endless migrations and disruptions just to keep up.
In practice, this means looking at systems that can expand horizontally, not just “scale up.” A parallel file system is often the backbone here because it adds throughput and concurrency as you add nodes, which is exactly what GPU clusters need. Alongside it, object storage provides virtually limitless capacity for raw data lakes and long-term retention, making it easier to handle the sheer volume without constantly pruning datasets. Together, they let IT leaders plan for growth without fearing the ceiling.
#2 Performance
Performance is where AI storage makes or breaks the project. Training models involves thousands of cores hitting the same data at once, while inference often needs quick responses in real time. If data doesn’t flow fast enough, GPUs stall, and timelines stretch — an expensive problem when every hour of idle compute burns budget.
This is where a parallel file system shines, because it delivers the concurrent, low-latency access that training clusters expect. The goal isn’t just high IOPS on paper; it’s making sure every GPU stays busy. Object storage usually isn’t the tier feeding training directly, but it plays a key role in staging and archiving. Having a smooth handoff between these layers means high-performance data like active training sets, checkpoints, and validation batches is always where it’s needed, and everything else such as raw ingests, archived versions, and rarely accessed historical datasets sits on more economical media.
#3 Data Management & Organization
As datasets multiply, managing them becomes as critical as storing them. Teams need to know which dataset is the latest, which one was used to train a particular model, and whether older versions are safe to delete. Without discipline, storage fills with duplicates, stale copies, and half-finished experiments, slowing projects and wasting capacity.
A common best practice is to keep the system of record in an object store, where rich metadata and lifecycle policies make organization easier, then promote curated datasets into a parallel file system for active use. Increasingly, organizations are relying on data orchestration layers that automatically place and move datasets across file, object, and cloud tiers while presenting them under a single namespace. This ensures that the teams building and training models always see and access their data seamlessly, while storage operates efficiently in the background.
#4 Integration with AI & Data Pipelines
AI doesn’t run in a vacuum; it runs inside frameworks, schedulers, and analytics engines. If storage doesn’t “speak their language,” teams end up building workarounds that slow everything down. Seamless integration is less about flashy features and more about making data available in the right way to the right tools.
Here, parallel file systems are critical because they align with the high-performance compute side of AI, supporting POSIX access and parallel I/O libraries. Object storage complements by feeding data lakes, ingestion frameworks, and analytics tools that prepare or preprocess data. When both layers work together, data flows naturally from ingestion to training to deployment — without endless copying and manual staging.
#5 Security, Compliance & Data Trust
AI projects often involve data that organizations can’t afford to mishandle whether it’s personal information, financial transactions, or intellectual property. The storage layer has to protect this data, but it also has to prove it is protected, especially in industries with regulatory oversight.
Object storage has a natural advantage here because of features like immutability, audit logging, and policy enforcement that help with compliance. Parallel file systems add value during the “live” phases of model building by supporting secure multi-user access and reproducible datasets. The combination means sensitive data is protected whether it is archived for the long haul or actively fueling model training.
#6 Cost & Sustainability
AI storage can quickly become one of the most expensive line items in IT. Keeping every dataset on the fastest media is unsustainable, and so is slowing down GPUs because storage is underpowered. Cost efficiency comes from matching data to the right tier, balancing speed, scale, and sustainability.
In practice, this usually means keeping hot datasets on high-performance file systems, while shifting inactive or historical data to object storage where cost per terabyte is lower. Beyond cost, efficiency now also means energy efficiency: modern architectures that reduce hardware sprawl and automate tiering help organizations lower both spend and carbon footprint. This is where smart design pays off twice: faster AI outcomes at a lower long-term cost.
How Storage Powers the AI Data Pipeline
AI processing typically involves multiple stages, each with distinct storage requirements that impact the overall efficiency and effectiveness of AI initiatives. While the specifics of AI pipelines can vary depending on the application and industry, the following stages outline a common approach. Optimizing storage for each phase is crucial to ensuring smooth and efficient AI operations.
Data Collection and Ingestion
At the outset, raw data flows into the system from various sources such as sensors, databases, and external APIs. The storage infrastructure must support massive scalability to manage the continuous influx of diverse datasets. Efficiently handling large volumes of data without performance bottlenecks is essential, ensuring smooth data ingestion as AI projects scale.
Data Pre-processing and Refinement
During pre-processing, data is transformed, cleaned, and structured, demanding storage systems that provide consistent high I/O performance. This stage requires the ability to handle frequent, intensive read/write operations across various data types. Support for high concurrency is also critical, enabling multiple data processing tasks to occur simultaneously without impacting overall system performance.
Model Training and Checkpointing
Model training is one of the most storage-intensive stages in the AI pipeline. During this phase, large volumes of refined data are fed into machine learning algorithms, requiring high-throughput and low-latency storage. Additionally, checkpointing—saving intermediate versions of the model during training—demands reliable and fast storage to ensure data integrity and continuity. Parallel processing at the storage level can enhance the efficiency of this process by keeping up with the computational demands.
Model Evaluation and Testing
In the evaluation phase, the focus is on quick, reliable data access to validate model performance. Fast retrieval of both trained models and validation datasets is essential to avoid delays in testing cycles, enabling rapid iteration and refinement of AI models.
Model Deployment and Inference
For real-time inference, storage systems must deliver low-latency data access to ensure timely and accurate predictions. Scalability is also important at this stage, as inference workloads can vary significantly, requiring the storage infrastructure to adapt to changing demands without disruption.
Data Provenance, Audit, and Monitoring
Ensuring data integrity and comprehensive metadata management is critical in the final stage, where maintaining accurate records of datasets and model versions is essential for compliance and auditing. Robust data management practices, including versioning and traceability, support the ongoing monitoring and refinement of AI models.
Building an AI-Ready Storage Foundation
Preparing storage for AI isn’t just about speed or scale in isolation. It’s about ensuring that data flows efficiently through every stage of the pipeline. From high-performance training to long-term retention of massive datasets, the right foundation enables organizations to make the most of their compute investments while keeping costs sustainable. DataCore provides two complementary storage technologies that together deliver both sides of the equation.
Performance at Scale for AI Workloads
DataCore Nexus is built to meet the extreme demands of AI training and inference. Unlike traditional storage, it delivers the parallel file system performance required when many GPUs and compute nodes need to hit the same dataset simultaneously. Powered by NVMe technology, Nexus achieves exceptional throughput — up to 180 GB/s in just a 4U form factor — ensuring compute clusters are never left waiting on data. For AI workloads, this translates directly into faster model training, quicker checkpointing, and the ability to iterate at the pace innovation requires.
Beyond raw performance, Nexus also provides intelligent data mobility. As datasets evolve, they can be automatically promoted into the high-speed file system during active use and then seamlessly tiered off to lower-cost capacity when they cool, all while remaining accessible through a single namespace. This orchestration eliminates manual data shuffling and keeps workflows smooth, making Nexus not just fast, but also efficient in how it supports the end-to-end AI pipeline.
Long-Term Data Retention for AI
As valuable as high-performance access is, the majority of AI data eventually transitions into a colder state: raw ingests, prior training runs, historical versions, and compliance records. These datasets must be preserved for traceability, re-use, and audit, but it makes little sense to keep them on the fastest and most expensive storage.
DataCore Swarm addresses this need with a massively scalable, software-defined object storage layer that provides economical, long-term retention without sacrificing durability or accessibility. It ensures that AI archives remain secure, compliant, and ready to be tapped back into the pipeline if needed. By offloading inactive data into Swarm, organizations can keep their high-performance environments lean and focused, while still preserving the full depth of their AI history.
Ready to prepare your storage infrastructure for the AI future? Contact DataCore to discuss how we can help you build the foundation for innovation.
