Why Storage is Critical for AI Inferencing

(Even If Some Analysts Say Otherwise)

For the past few years, AI infrastructure discussions have been dominated by model training. Enterprises poured billions into GPU clusters, high-speed networking, and parallel computing to train larger and more complex models. But the industry is shifting. Training is no longer the primary challenge—deploying AI in real-world applications is.

That means the focus is moving to inferencing—where AI models generate insights, power AI agents, and drive intelligent automation at scale.

Yet, when I talk to analysts about AI inferencing, I keep hearing a recurring theme from some of them: “Storage doesn’t matter for inferencing.”

This is a frustratingly short-sighted take. Many assume that once a model is trained, storage becomes irrelevant. But as inferencing evolves—especially with AI agents, Retrieval-Augmented Generation (RAG), and real-time decision-making—the importance of high-performance storage becomes undeniable.

And this misconception ignores a fundamental reality – storage is often the biggest source of latency in AI inferencing, and latency kills user experience. Worse yet, overcoming storage-induced latency is vastly expensive, forcing enterprises to overprovision compute just to compensate for slow data access.

AI Inferencing is More Storage-Intensive Than You Think

Inferencing isn’t just about loading a model into memory and running calculations. Modern AI inferencing is dynamic, data-intensive, and continuous. Unlike training, which is a batch process, inferencing requires always-on, high-speed data access to serve real-time AI applications, including:

1. AI Agents Rely on Constant, Unpredictable Data Access

AI agents—whether for business automation, cybersecurity, or real-time recommendations—don’t just run pre-trained models in isolation. They:

• Continuously retrieve, process, and generate new data
• Access diverse data sources in structured and unstructured formats
• Need high metadata performance to quickly look up and manipulate small files

These workloads generate millions to billions of small files and require rapid metadata handling—an area where many storage solutions fall short.

2. Retrieval-Augmented Generation (RAG) Introduces New Storage Challenges

RAG is reshaping inferencing by improving AI model responses with real-time retrieval of external data. Instead of relying solely on static, pre-trained knowledge, RAG dynamically pulls in the most relevant information from:

• Vector databases for semantic search
• Enterprise document repositories
• Streaming datasets and real-time feeds

This means that inferencing isn’t just about running a model—it’s about searching, retrieving, and integrating information on the fly. Storage needs to be:

• Fast enough to feed AI models without bottlenecks
• Scalable enough to handle multi-petabyte datasets
• Flexible enough to support structured and unstructured data

3. AI Inferencing is Multi-Modal and Multi-Tenant

Inferencing workloads today aren’t uniform—they involve a mix of:

• Batch and real-time inference
• Small and large model serving
• Structured (databases) and unstructured (text, video, sensor) data

In multi-tenant AI environments, where different teams and models access shared infrastructure, storage performance can make or break AI inferencing at scale. Legacy storage struggles with:

• Data locality issues when inferencing happens across cloud and on-prem environments
• High concurrency demands as multiple AI workloads compete for access
• Scalability limitations when handling both large models and high-throughput inferencing

4. Inferencing Workloads Have Highly Random I/O Patterns

Unlike training workloads, which process large volumes of data in sequential, high-throughput pipelines, inferencing workloads are often:

• Highly fragmented and random in their I/O patterns
• Unpredictable in access patterns, especially for multi-tenant AI environments
• More dependent on metadata and small file access than raw throughput

The AI Infrastructure Shift: From Training to Inferencing

With AI moving into real-world deployment, the industry is realizing that the inferencing bottleneck isn’t compute—it’s data access. While GPUs handle the model execution, they’re useless if the data they need isn’t available in real time.

The companies that solve the inferencing storage challenge will:

• Deploy AI models more efficiently
• Scale AI agents and RAG workloads without performance drops
• Reduce infrastructure costs by eliminating unnecessary GPU idle time

How WEKA Supercharges AI Inferencing

WEKA’s data platform was built for AI workloads, ensuring that inferencing happens at full speed without bottlenecks. Unlike legacy storage, WEKA:

• Delivers ultra-fast, low-latency access to AI models, inference data, and RAG sources
• Optimized for extreme small-block, highly random? I/O, it excels in areas that matter most for AI inferencing:
• Optimizes vector search and knowledge retrieval for AI agents and RAG-based inferencing
• Provides a unified data layer across on-prem, cloud, and hybrid environments
• Scales seamlessly to handle petabyte-scale AI workloads without performance degradation

AI’s future isn’t just about bigger models—it’s about smarter, faster, and more efficient inferencing. Storage is the key to making that happen, and WEKA is here to help.