Since the dawn of the mainframe and the birth of the PC, computing has seen endless transformations. Through every evolution, from floppy disks to NAS appliances, SAN solutions, cloud drives, and now voracious AI workloads, one truth remains. We never seem to have enough storage. But the challenge isn’t just capacity, it’s also speed. The difference between a responsive application and a sluggish disappointment often comes down to how quickly data can be accessed. If your storage is slow, then so are your applications. With the growing reliance on AI driven apps, this is truer than ever.
The Real Time Nature of AI Demands Storage
In the new era of AI, GPU chips often steal the spotlight, dominating headlines and discussions. Storage seems to be the unsung hero that’s just as critical, especially for AI and large language models. While storage may not be the first thing that comes to mind, it’s just as essential as it ever was, maybe more so. For example, chatbots are expected to deliver real-time, conversational responses, and not delayed replies like on traditional discussion boards. This demand for instant interaction is reshaping storage architecture in several important ways:
- Real-time AI apps such as those for live analytics, autonomous vehicles, or instant recommendations require storage that can deliver data with minimal delay. This means storage systems must be designed for extremely low latency and high availability as bottlenecks and downtime cannot be tolerated.
- Real-time AI applications typically use in-memory databases that store data in RAM for microsecond response times, along with high-speed NVMe storage to minimize latency and support demanding workloads.
- AI workloads can experience sudden demand spikes which means that storage architectures must be able to scale horizontally by adding more servers or nodes for proper load distribution.
- AI applications often need continuous data from IoT devices, user interactions, and other real-time sources, requiring storage systems to efficiently capture and feed this streaming data directly to AI models for immediate processing.
Storage Varies by Use Cases
As in all things, resource loads vary based for different use cases. There is a lot more to AI than asking ChatGPT questions or having Copilot summarize a long email thread. Here are some examples:
- AI training requires high capacity and ample throughput as the storage requirements are immense. A vast number of datasets are used including text, images, videos, and sensory data to name a few.
- AI inference prioritizes speed and reliability over data volume. While inference datasets are smaller than training sets, fast response time is imperative for use cases such as image recognition, fraud detection, recommendation engines, or self-driving cars.
- Chatbots require storage systems that can instantly retrieve conversation history and context while serving thousands of simultaneous users, ensuring each interaction feels natural and responsive without delays that would break the conversational flow.
How RAG Affects Storage
Traditional applications work with predictable file types like Word documents or images, but large language models must process vast, chaotic datasets filled with unstructured information. These models also draw data from everything from social media posts to research papers to outdated records that may no longer reflect current reality. This creates significant challenges for LLMs.
Retrieval-Augmented Generation (RAG) overcomes these challenges by enabling LLMs to search and utilize specific datasets rather than relying solely on internet data or pre-trained knowledge. However, implementing RAG demands substantial storage because it relies on storing and processing vast amounts of data that often reaches petabyte scale to properly vectorize the data. This can increase your storage demands by up to 10 times beyond the original data size.
What is TRISM
Like any enterprise technology, the subject of cybersecurity must always come up. In terms of AI that includes trust, risk, and security management (TRISM). Developed by Gartner, the AI TRiSM framework addresses these critical security components across the entire AI system lifecycle. It is structured around four key layers:
- AI Governance
- Runtime Inspection & Enforcement
- Information Governance
- Infrastructure & Stack
When it comes to storage serving the needs of AI, capacity and performance are only part of the puzzle. TRiSM introduces multiple considerations to safeguard data integrity, security, and compliance.
- Storage systems must include robust security features like encryption, access controls, and multi-factor authentication to protect sensitive AI data and models from unauthorized access or manipulation.
- Storage should also include detailed logging, data lineage, and audit trails to meet regulatory requirements and enable transparent reporting.
- Must support data classification (sensitive, personal, or regulated data) and enforce retention, deletion, and purpose-based access policies.
How Evolving Solutions can Help
AI may be proving transformative, but at its core, it’s another mission-critical workload that demands the right infrastructure foundation. At Evolving Solutions, our dedicated team specializes not just in storage, but in storage solutions purpose-built for AI environments. Whether you need help designing your architecture from the ground up or simply want an expert assessment to optimize your existing infrastructure, our AI storage architects and engineers are ready to support you at any stage of your journey.
Regardless of the extent of our involvement, our approach always begins with a deep understanding of your unique business and technology objectives, because we believe technology should always serve your business goals. Don’t let storage become the bottleneck that limits your AI potential. Contact us today to ensure your infrastructure can deliver on AI’s promise.
