Last year, 72% of organizations integrated AI into at least one business function, up from 55% in 2023. This shift sees technology leaders rethinking their approach to their IT infrastructure. AI workloads demand significant computational power, high-speed networking, as well as scalable and efficient storage solutions. Legacy systems often struggle to meet these demands.
One area that requires significant attention is block storage. While legacy SAN/NAS systems can still be used for certain aspects of AI workflows, they are not ideally suited for the most demanding AI workloads that require high throughput, low latency, and massive parallel access. Modern storage solutions, such as those based on NVMe over TCP and software-defined architectures, are better equipped to handle these requirements.
The Storage Challenge in AI
The AI data pipeline, encompassing everything from model training to inference, presents unique challenges for storage systems. AI workloads require high-performance, low-latency storage solutions capable of managing vast amounts of structured and unstructured data. Traditional storage architectures often fall short in meeting these requirements, creating a significant bottleneck in the AI workflow.
One of the critical aspects of AI workloads is the need for high-speed data access. This is particularly important for inference processes, where AI models need to quickly access and process data to make real-time decisions. Vector databases, which play a crucial role in retrieval-augmented generation (RAG) models, also demand high-performance storage to efficiently store and retrieve embeddings.
Unfortunately, storage is often an afterthought in discussions about AI infrastructure. However, the reality is that storage plays a pivotal role in determining the overall performance and cost-efficiency of AI deployments. Traditional storage architectures, designed for predictable I/O patterns of conventional applications, struggle with the sheer volume and velocity of data required by AI models. This mismatch leads to bottlenecks that can significantly slow down model training and inference processes.
The Hidden Costs of Inefficient Storage
Many organizations are inadvertently wasting resources on inefficient AI storage solutions that were not purpose-built for AI workloads. This results in underutilized, high-cost storage systems that fail to deliver the required throughput and performance. Bottlenecks in data retrieval and inference can significantly slow down AI applications, impacting real-time processing capabilities and overall efficiency.
Furthermore, inefficient scaling models can compound these issues. As organizations seek to expand their AI initiatives and scale their machine learning operations, the costs associated with accommodating growing storage needs can become prohibitive. This challenge is exacerbated by the fact that AI workloads differ significantly from traditional IT workloads.
Traditional storage architectures, such as legacy SAN or NAS systems, often introduce latency that hinders AI performance. This latency can be particularly detrimental in environments where real-time or near-real-time data processing is critical, such as autonomous systems, fraud detection, and personalized recommendations.
The Role of Block Storage
To address the storage challenges posed by AI workloads, a smarter and leaner approach is necessary. Instead of relying on monolithic, legacy architectures, businesses need to embrace software-defined storage solutions that are optimized for the unique demands of AI applications.
Block storage has emerged as a powerful tool in this regard. By leveraging high-performance NVMe/TCP-based, software-defined block storage, AI workloads can access data more efficiently, reducing delays in model training and inference. Software-defined architectures enable better resource allocation and scaling, allowing organizations to dynamically adjust their storage needs based on workload demands. This ability to disaggregate compute and storage ensures that AI deployments remain cost-effective while achieving superior performance.
NVMe/TCP-based storage architectures provide a cost-effective way to achieve high-performance AI storage. It offers near-direct-attached storage (DAS) speeds while maintaining the flexibility of disaggregated storage.
Benefits of Block Storage for AI
Software-defined block storage architected for NVMe/TCP offers several key benefits for AI workloads:
- Reduced Latency: it provides faster read/write speeds and lower latency compared to traditional storage systems. This ensures that AI models can process information with minimal delay, which is crucial for real-time applications.
- High Concurrency: it is optimized to handle concurrent data access efficiently. This makes it an ideal choice for AI applications that rely on rapid retrieval and processing of large volumes of data.
- Scalability: it allows for independent scaling of storage resources, decoupled from compute resources. This enables organizations to expand their AI capabilities without unnecessary investment in hardware upgrades.
- Cost-Effectiveness: it provides near-DAS speeds while maintaining the flexibility of disaggregated storage.
- Enhanced Flexibility: it allows for dynamic allocation of storage resources, ensuring optimal performance for AI workloads. This flexibility enables organizations to adapt their storage infrastructure to the evolving needs of their AI initiatives.
A Practical Approach to Infrastructure Modernization
The path to AI architecture modernization should be done using a phased approach. Firstly, enterprises need to evaluate their existing technology stack to identify inefficiencies in compute power, networking limitations, and storage bottlenecks that can hinder AI performance. To do so requires a comprehensive assessment of current workloads, data management strategies, and processing speed to ensure that all infrastructure components align with AI’s unique demands.
Beyond that, decision-makers must consider how best to shift towards disaggregated compute and storage architectures. Traditional monolithic storage solutions often create dependencies that limit scalability and flexibility. By adopting software-defined storage and NVMe/TCP, enterprises can improve their ability to scale storage independently of compute, ensuring that AI workloads can expand without requiring costly hardware overhauls.
Optimizing storage for AI inference and vector databases is another essential step in the modernization journey. AI models require rapid access to vast datasets, and inefficient storage architectures can create latency that slows down real-time applications. High-throughput block storage enables AI inference at scale, ensuring that models can retrieve and process information instantly. Additionally, retrieval-augmented generation models benefit from low-latency storage solutions that enhance query performance and knowledge extraction, making AI-driven insights more actionable.
To validate and refine infrastructure upgrades, businesses should incorporate industry benchmarks, such as the MLPerf benchmark, into their processes. Such benchmarks will deliver an objective assessment of AI storage performance, helping enterprises make informed decisions about technology investments. By continuously optimizing storage and compute environments, companies can ensure that their infrastructure remains competitive, agile, and capable of handling future advancements in AI.
No Turning Back
Modernizing IT infrastructure extends beyond upgrading compute and networking capabilities. Organizations should see this as an opportunity to rethink how data is stored, accessed, and managed. Organizations need to adopt a comprehensive AI strategy that incorporates storage solutions specifically designed to handle the intense data demands of modern AI workloads.
Businesses that invest in software-defined, NVMe/TCP-based block storage solutions will be well-equipped to overcome performance bottlenecks, streamline operations, and scale AI applications efficiently. In today’s competitive landscape, prioritizing infrastructure modernization is essential for unlocking the full potential of AI initiatives. By doing so, organizations can not only achieve new levels of innovation but also ensure the long-term sustainability of their AI investments.
