Storage: The Overlooked Part of the AI Stack

AI is advancing fast. Meta launched Llama 3. OpenAI introduced GPT-4o. Elon Musk's Grok is making headlines. And NVIDIA's new Blackwell platform is promising performance gains that will push AI even further.

But as the industry focuses on bigger models and more powerful GPUs, one thing keeps getting overlooked: storage.

Behind every AI breakthrough is a massive flow of data. And if the infrastructure that moves and manages that data can't keep up, it doesn't matter how powerful the models are; everything slows down or breaks.

AI Runs on Data, and Data Needs Infrastructure

Training large language models requires petabytes of data. Inference workloads, especially those that use techniques like retrieval-augmented generation (RAG), need real-time access to structured and unstructured datasets.

If the storage system can't deliver that data fast enough, the model becomes less responsive, less useful, and more expensive to run.

Today's AI systems need storage that is:

• Built for speed (high throughput and IOPS)
• Scalable across multiple users and workloads (parallel access)
• Integrated directly with compute (e.g. NVMe-over-Fabrics)
• Designed for real-time updates and dynamic workloads

These are no longer edge-case requirements. They're now mission-critical infrastructure for anyone running serious AI.

Where It's Already Happening

1/ xAI: Grok is reportedly trained on real-time X content. This means it requires constant data ingestion, indexing, and low-latency retrieval — exactly the kind of workload where traditional storage solutions would fall short.

2/ NVIDIA and DDN: The Blackwell platform enables trillion-parameter models. Feeding those GPUs at full speed requires massive bandwidth. That's where DDN comes in, offering storage systems certified by NVIDIA that deliver up to 600GB/s throughput for high-performance AI environments.

3/ Google Cloud and DDN: For organizations building AI in the cloud, Google's managed Lustre service (powered by DDN EXAScaler) provides high-performance storage for training, inference, and RAG pipelines without the need to manage infrastructure directly.

A Familiar Pattern

We've seen this before in other industries. In electric vehicles, everyone focused on design and features, until it became clear that battery technology was the real enabler. The same thing happened in gaming when solid-state drives unlocked faster, smoother experiences.

And it happens in everyday systems too. Think about plumbing. Nobody notices it when it works. But when it doesn't? Everything stops. The same is true for storage in the AI stack.

The Takeaway

GPUs and models are important. But they only matter if the data can reach them in time.

Storage is no longer a background system; it's part of the core architecture. It enables performance, lowers cost, and unlocks what's possible with modern AI. If you're building or scaling AI, storage shouldn't be an afterthought. It should be one of your first decisions.