Inference at Scale – Storage as the New AI Battleground

TL;DR Artificial Intelligence has a new center of gravity: inference at scale. AI inference at scale operates like running thousands of daily flights rather than building a single airplane, requiring constant, millisecond-response operations. Storage infrastructure has emerged as the new competitive battleground, where ultra-low latency data access determines whether AI services succeed or fail in delivering seamless user experiences. Organizations that architect their storage systems for high-IOPS, low-latency workloads will gain strategic advantages in the inference-driven AI economy.

For years, the focus in AI infrastructure was on model training. Training is heavy on compute and benefits from high-throughput data access, but it was largely a background process – something you do in the lab, then deploy the model. Inference is the deployed model in action: answering a chatbot query, recommending a product, flagging a fraudulent transaction, etc. 

What’s changed recently is the scale and importance of inference:

  • Always-On, Real-Time: Inference now has to happen in milliseconds, because it’s often in the critical path of user interactions. If your AI-driven customer support agent or search bar is slow, user experience suffers.
  • High Volume: A single trained model might serve billions of requests (think of a language model integrated into a popular app). That’s billions of small, random data accesses as the model retrieves facts, writes logs, and updates caches.
  • Unpredictable Load: Traffic patterns for AI services can spike with news events or viral trends. One minute your system is handling 10 requests per second, the next minute a new feature launches and it’s 10,000 per second. The infrastructure must absorb that without falling over.

What does this mean for storage? In one word: latency. 

While training was about feeding GPUs lots of data (throughput), inference is about handling many tiny reads/writes extremely fast (IOPS and latency). A user waiting for an AI response won’t tolerate a long delay. So the storage system backing your AI must excel at low-latency, high-IOPS workloads – a profile more akin to high-frequency trading systems than traditional analytics.

Why Storage Performance Makes or Breaks AI Inference Success

In this new world of inference at scale, I’ve seen storage be the make-or-break factor. Here are a couple of real-world observations:

  • A financial services company recently integrated AI models into their mobile application for millions of users. Small-scale trials performed excellently, but full deployment created inconsistent response times. Investigation revealed that storage backends couldn’t handle the sudden increase in random read operations needed to fetch user-specific data and model files for each session. GPU performance remained optimal and application code functioned correctly, but the outdated storage infrastructure created a massive data bottleneck.
  • Conversely, a large e-commerce platform built its AI inference architecture on modern, high-performance distributed storage systems from initial deployment. The company architected its storage infrastructure as a first-class component of the AI stack. During holiday traffic surges that activated the service’s AI recommendation engine at maximum capacity, performance remained consistent. Low-latency data access maintained smooth AI pipeline operations, resulting in increased sales, satisfied customers, and stable system performance despite the unpredictable nature of AI inferencing.

The difference between these scenarios underscores a key point: Storage can be either the bottleneck or the secret weapon for AI services. If you invest in it and architect it right, it becomes a strategic advantage that enables you to scale AI capabilities quickly and reliably. If you ignore it, it will come back to bite you when you least expect it.

What Are the Key Storage Requirements for AI Inference Workloads?

Storage solutions that deliver Inferencing at scale must satisfy five fundamental requirements that differ significantly from traditional enterprise storage needs:

1. Ultra-Low Latency Data Access We’re talking microseconds to low milliseconds. This often means using all-flash storage, NVMe drives, and even placing some data in-memory or in GPU memory (read more about that here) when possible. It also means minimizing network hops – technologies like NVMe-over-Fabrics (NVMe-oF) and RDMA can help ensure that the trip from storage to GPU is as short as possible.
2. High IOPS and Concurrency The system must handle a massive number of small I/O operations at the same time. Parallelism is key – scale-out storage architectures that distribute load across many nodes can achieve this far better than a monolithic storage array. Also, modern IO schedulers and optimization (like NVIDIA’s GPUDirect Storage, which cuts out CPU overhead) come into play here.
3. Scalability and Elasticity You should be able to scale capacity and performance independently, and do so quickly. Need more IOPS? Add a few more storage nodes or NVMe drives and the system should automatically rebalance. Similarly, if your data footprint doubles because you’re logging every inference for auditing (which is common in regulated industries), you should be able to add capacity without a forklift upgrade.
4. Reliability under Load Inference doesn’t pause. Unlike training, where you might be able to redo a job if something fails, inference is live. The storage can’t have hiccups or long failover times. Distributed, fault-tolerant designs and features like replication or erasure coding help ensure that even if a node or drive dies, the service continues uninterrupted.
5. Integration with AI Workflows This is a softer factor, but important. The storage system should play nicely with your AI stack. For example, if you’re using an inference server (like NVIDIA Triton or similar), the storage should support the protocols and patterns those servers use (e.g., lots of opens/closes, caching of model files, etc.). If you’re utilizing new techniques like RAG or KV caching (more on these in a bit), the storage must support fast search and fast read/write of lots of small objects.

How Do RAG and Distributed Caching Transform Storage Architecture Demands?

During a recent panel with SuperMicro, we discussed Retrieval Augmented Generation (RAG) and distributed Key-Value (KV) Cache as two game-changers for inference. Let me briefly touch on why they matter:

  • RAG: This technique appends relevant data from an external store into an AI model’s input to make answers more accurate and up-to-date. For storage, it means you now have potentially millions of documents or embeddings that need to be searched and retrieved in real time for each query. That’s a search problem (vector databases, etc.) married to a storage problem (those documents/embeddings live on some medium). Storage systems for RAG need to support very high read throughput and fast random access. Oftentimes, the “external knowledge” is stored on an object store or a specialized database, but the principle remains: the latency of fetching that info will directly affect your AI’s response time.
  • Distributed KV Cache: When your AI model handles a conversation or a repetitive task, it can cache intermediate results (in key-value form) to avoid redundant computation. In practice, this might be a cache that lives on NVMe storage accessible by all your AI servers. The idea is to trade a bit of storage space to save a lot of computation time.

WEKA’s Inference Solutions for AI at Scale

WEKA AI Reference Platform (WARP)

Production-grade RAG implementation requires careful attention to deployment flexibility and scalability. The WEKA AI Reference Platform (WARP) addresses these challenges through a modular, production-tested architecture designed for enterprise-scale RAG deployments. 

WARP reflects the reality that moving AI from laboratory experiments to production environments creates entirely new infrastructure demands: workload variability where different tasks compete for resources, scaling pain when rigid designs prevent adaptation, and the need for continuous optimization without system downtime.

WEKA’s Augmented Memory Grid 

Advanced storage platforms now incorporate features that transform storage software into large-scale, fast, distributed key-value stores specifically for AI data. WEKA’s Augmented Memory Grid completely changes AI inference economics by providing petabytes of persistent storage for KV cache, enabling dramatic improvements in time-to-first-token performance. 

We’ve shown up to a 4.2x throughput gain for certain inference workloads and a reduction in infrastructure cost. But implementing this means your storage layer becomes part of the memory hierarchy – it’s got to be fast and distributed. This essentially turns our storage software into a giant, fast, distributed KV store for AI data.

Powered by NeuralMesh Architecture

This innovation builds upon WEKA’s NeuralMesh architecture, which uses containerized microservices to create a service-oriented design that interconnects data, compute, and AI infrastructure and includes three key components:

  • The Core component creates resilient storage environments that improve at petabyte scale
  • The Accelerate component ensures microsecond latency at exabyte scale by creating direct paths between data and applications
  • The Deploy component provides maximum deployment flexibility from bare metal to multi-cloud environments, enabling organizations to implement advanced caching strategies across diverse infrastructure configurations.

The containerized microservices architecture of NeuralMesh enables true multi-tenancy with complete isolation between AI workloads, horizontal scaling based on inference demands, and operational flexibility to maintain quality of service during updates and maintenance. This foundation supports both the advanced caching techniques required for RAG and distributed KV cache while ensuring storage infrastructure can scale seamlessly with growing AI inference demands. This eliminates the data bottlenecks that prevent GPUs from operating at peak utilization.

This trend reflects the industry’s recognition that storage must evolve beyond traditional file and block services to support AI-specific caching and retrieval patterns, with more vendors expected to follow this approach across the storage industry.

Strategic Questions Technology Leaders Should Ask About AI Storage Infrastructure

If you’re a technology leader reading this, you might be thinking, “Why should I care exactly how storage works, as long as it works?” 

Here’s why: as AI becomes integral to products and services, the differentiator between winners and losers may well be how well their infrastructure supports AI at scale. We’re beyond theory. This is about delivering consistent, fast, intelligent experiences to customers or insights to decision-makers. Inference at scale is where the rubber meets the road. If your competitor’s app feels more responsive or their AI can incorporate real-time data and yours can’t, that’s an edge that can translate to market share.

Therefore, it’s worth getting involved in these architecture discussions or at least asking the right questions:

  • Have we architected our data pipeline to minimize latency for our AI use cases?
  • Are we confident we can scale up the inference side as aggressively as we can scale up model training or user adoption?
  • What’s our plan for disaster recovery and failover for the AI services – does our data layer support that, or is it a single point of failure?
  • How are we managing costs as we scale? (All-flash is great but also expensive; a tiered approach can rein in costs if done right, without sacrificing too much performance.)

How Will Storage Infrastructure Define Competitive Advantage in the AI Economy?

According to MIT Technology Review, 80–90% of AI compute usage is now driven by inference, not training, emphasizing the critical importance of optimizing infrastructure for real-time deployment rather than development workloads.

In this phase, the spotlight shifts to storage and data. A modern AI-driven business needs storage that’s as agile and powerful as the AI models it serves. It’s no exaggeration to say that in the coming years, we’ll see competitive advantage defined not just by who has the best model, but also by who has the best infrastructure delivering that model’s intelligence to the world. By recognizing that storage is a critical piece of the AI puzzle and investing in the right architecture – high-performance where it counts, tiered for efficiency, scalable and future-proof – organizations can position themselves to thrive in the new battleground of AI inference.

I encourage you to read this report that we recently released with Nand Research. It goes into detail about the impact of storage across the entire AI lifecycle – from ELT to training to inference to agentic AI outcomes.

My colleagues are also having calls with companies running inference workloads and, depending on where you are, may be able to conduct a cost analysis or advise on how to optimize your roadmap.