What is AI storage?

AI innovation is accelerating rapidly, and AI projects require storage architecture that can both accommodate expanding data growth and deliver the performance, scalability and low-latency access that AI-driven workloads demand.

According to a study by Precedence Research, the global AI-powered storage market is estimated to grow from USD 35.95 billion in 2025 to approximately USD 255.24 billion by 2034. The estimated compound annual growth rate (CAGR) is of 24.42%.¹ The accelerated integration of AI and ML, along with the rise in AI storage use cases across industries, is driving market growth.

Enterprises are modernizing their data storage infrastructure to harness the business potential of AI, ML and advanced analytics. Yet, they’re challenged by data and workloads distributed across multiple regions, the increased time required for AI training and inferencing workloads. To these issues, adds the cost and scarcity of on-demand resources like graphics processing units (GPUs). 

According to an IBM Institute for Business Value (IBV) study, 62% of executives expect to use AI across their organizations within 3 years. However, only 8% said that their IT infrastructure meets all of their AI needs.

Looking toward the future, only 42% of those surveyed believe that this infrastructure can manage the data volumes and compute demands of advanced AI models. Similarly, only 46% expect it to support real-time inferencing at scale

AI workloads require systems that can reduce data-processing bottlenecks, which slow down model training, fine-tuning and inference. They also need scalable storage systems to handle ever-growing datasets, particularly those associated with generative AI and large language model (LLM) workloads.

To meet these demands, AI storage can integrate seamlessly with open source and proprietary ML and deep learning frameworks through application programming interfaces (APIs). This capability accelerates LLM training, model development and enhances overall performance across the AI system.

To learn more, check out: “Infrastructure for AI: Why storage matters.”

Traditional data storage is used for general business apps, whereas AI storage provides the foundation for training and running complex, data-intensive AI models efficiently and cost-effectively.

While traditional storage deals with both structured and unstructured data, it’s designed for typical business workloads with predictable patterns, not for training models on distributed systems and running inference at scale.

AI storage refers to the systems used to store and manage data for training and running AI infrastructure systems, including data lakes, cloud storage and databases. It handles massive volumes of unstructured data (for example, images, audio, video, sensor data).

These types of data require a storage system that delivers high IOPS (input/output operations per second) and ultra-low latency, especially during model training and inference.

In sum, the key difference between traditional storage and AI storage boils down to workload specifications. Traditional storage was built for consistent, predictable operations, while AI workloads have unique, demanding requirements across their entire lifecycle.

Each stage of the AI system lifecycle—data ingestion, training, inference and model updates—has unique storage needs, demanding petabytes of storage capacity and high-speed memory.

AI storage uses data pipelines to facilitate continuous data flow, from collection through preprocessing to model consumption. It uses scalable architectures, including object storage and parallel file systems, which process data in parallel across multiple storage nodes. This capability allows AI applications to handle real-time data at the high speed required.

To balance cost and performance, AI storage typically involves storage tiers. Frequently accessed data (hot tier) is stored on high-speed cache and flash storage, while less-critical data (warm or cold) is stored on cost-effective, slower storage technologies for long-term retention.

• Flash-based storage: AI storage relies on solid-state drives (SSDs). These SSDs are semiconductor-based storage devices that typically use NAND flash memory for high performance, low latency and high throughput to process datasets for training and inference.

• Single-tier design: Most AI storage solutions use a single-tier architecture, providing a consolidated, unified environment for frequently accessed data. This type of architecture supports flash or SSD storage for low latency and high I/O performance.

• High-bandwidth memory (HBM): Data center GPUs, CPUs, accelerators and some SSDs use HBM to enable high-performance data transfer with lower power consumption than dynamic random-access memory (DRAM), a more traditional memory architecture.

• NVMe (non-volatile memory express) technology: NVMe, a protocol designed for highly parallel data transfer, plays a crucial role in AI storage. NVMe SSDs and networked storage (NVMe-oF) have the speed, programmability and capacity needed to support the massive parallel processing in AI workloads. 

• Data repositories: AI storage uses data platforms and data services (for example, data lakes, warehouses, lakehouses) to create a centralized environment for raw and unstructured data. This process breaks down silos and eliminates the need to move data between systems.

• Data reduction technologies: Data deduplication, compression and tiering techniques minimize an organization’s storage footprint and costs while maintaining the high-performance access required for AI workloads.

• Scalable environments: AI storage is deployed by using high-performance, scale-out infrastructure models, such as hybrid cloud, on-premises (on-prem), hyperscale data centers and edge environments.

AI storage provides key advantages that optimize AI workflows and infrastructure performance, including:

• GPU-accelerated performance: Supports GPU-accelerated applications and workloads, delivering the throughput needed for AI training and inference.

• Unified data access: Provides file, volume and object access across disparate data sources, including traditional storage, cloud and edge environments, eliminating the need to move data between systems.

• Data accessibility without movement: Enables access to data across multiple platforms and locations without physically moving it, reducing duplication and network costs.

• Automated data protection: Provisions data that uses policies and protection methods like encryption across environments, ensuring AI datasets are protected throughout their lifecycle.

• Hybrid cloud integration: Connects data from the data center to public cloud resources, improving application collaboration and bringing greater agility to AI workloads.

• Simplified storage management: Offers built-in scalability, automation and simplified operations, reducing complexity for AI initiatives.

• Cost optimization Eliminates data silos and duplication while merging compute and storage resources to lower infrastructure costs without compromising AI performance.

AI storage plays a crucial role in diverse, data-intensive AI, ML and high-performance computing (HPC) workflows. Further along are some industry-specific use cases:

• Retail

• Healthcare

• Finance

• Entertainment

• Manufacturing

• Insurance

Retailers use AI storage to manage large volumes of data and metadata generated by sales transactions, customer interactions, social media and IoT devices. This process enables real-time inventory optimization, personalized recommendations and demand forecasting.

In healthcare, AI storage accelerates drug discovery and supports clinical decision support through AI (for example, NVIDIA BioNeMo, IBM watsonx®) while handling enormous genomic datasets, medical imaging files and electronic health records.

Banks and other financial institutions rely on scalable AI storage to manage massive amounts of data from transaction volumes. This enables machine learning algorithms to detect patterns and anomalies across millions of transactions in real time, supporting fraud detection and personalized banking services.

Streaming services like Netflix and Amazon use AI data storage to process viewing history data at scale, enabling real-time recommendation engines that deliver personalized content.

AI storage provides data management for sensors and machines across factory floors. This infrastructure enables predictive maintenance, optimizes supply chains and automates quality control in real time.

AI storage supports automated underwriting and claims processing by enabling rapid access to documents, photos and unstructured data. This approach allows natural language processing (NLP) and image recognition models to accelerate risk assessment and expedite claims settlements.