Adoption of AI supercomputer environments such as the NVIDIA DGX SuperPOD is accelerating, but some organizations are already starting to experience “AI stall-out” — slowing development, plateauing results, and missed expectations. When this phenomenon occurs, the most common reaction is to invest in more GPUs in an attempt to increase the rate of data processing and model training. This approach, however, typically does little to address the root of the problem.
More than GPU bandwidth, AI infrastructure gaps — such as slow legacy data storage approaches — are the most likely culprit that stalls AI momentum. This is especially true for environments like the DGX SuperPOD, where ultra-high compute density demands extreme levels of data throughput.
In this article we’ll explore the needs of an NVIDIA DGX system for maximum efficiency, the costs associated with AI stall-out, and the characteristics of a balanced, AI-ready data storage architecture.
Understanding the Real Storage Requirements for AI
As they roll out their AI initiatives, enterprises often underestimate the role of AI data storage in the AI development process. Their existing data infrastructures typically aren’t equipped to handle the rigorous demands of AI.
AI-Native Data Storage
AI workloads rely on massive amounts of unstructured data, are read-heavy, and require ultra-low latency and parallel access. Therefore, to be suitable for AI development, a data storage infrastructure must be able to:
Handle high throughput with bandwidth capable of feeding petabytes of data to GPUs across hundreds of nodes.
Achieve low latency with immediate data availability, especially during AI model training and real-time inferencing.
Scale limitlessly as models grow and data multiplies, without requiring complex re-architectures.
Perform parallel tasks in order to concurrently handle the thousands of parallel file and object requests involved in an AI workload.
Why the NVIDIA DGX SuperPOD Needs More Than Just Speed
The NVIDIA DGX system — the leading platform used by enterprises for AI development — was built for high processing performance. However, without the right storage backend in place, that incredible compute power can never be fully leveraged.
The DGX SuperPOD architecture combines multiple NVIDIA DGX systems into a single massive AI supercomputer that’s capable of powering some of the largest AI models in the world. This architecture offers organizations immense GPU power, but in turn demands balanced input/output data performance across the stack — something only an AI-optimized storage layer can provide.
The Hidden Costs of an AI Stall-Out
As discussed above, there’s a lot more to the AI success equation than GPUs. In fact, GPUs are the most costly part of AI development for several reasons:
Organizations often underutilize expensive GPU infrastructure because the data layer can’t keep up (a common problem across the tech industry).
When GPUs sit idle waiting for data, organizations burn time and money.
Attempting to solve AI stall-out by purchasing more GPUs simply burns even more money, as GPUs are considerably more expensive than storage infrastructure.
The unfortunate result is that many organizations are investing millions in NVIDIA DGX systems, or even full SuperPODs, without also investing in a data pipeline that can actually keep up. And having made such substantial financial commitments, they can’t afford to experience AI stall-out and not reap the greatest possible business value from those strategic investments.
But companies that focus on setting up proper AI data storage first are bucking this trend. They’re able to better capitalize on their existing AI investments while avoiding unnecessary system expansion and additional spend.
Building a Balanced Architecture: What It Actually Looks Like
A modern, balanced data storage architecture designed to support the needs of AI systems such as NVIDIA DGX needs to prioritize three core traits: resilience, efficiency, and simplicity.
If you want to explore these ideas further, our team shares valuable insights in a detailed conversation about how a scalable SuperPOD solution can accelerate AI initiatives and help enterprises overcome common infrastructure challenges.
Resilience
At scale, occasional system errors such as temporary power loss or drive failure become inevitable. In a legacy data infrastructure, this type of failure at either the SSD or system level can take down the entire infrastructure, bringing AI development to a halt. Therefore, an organization’s modern data storage architecture has to be resilient enough to withstand these errors and keep moving forward.
The VAST AI Operating System (AI OS) was designed with resilience in mind from the start. It incorporates protective measures, such as fault-tolerant data enclosures, that provide enterprises with much greater peace of mind and AI development uptime, increasing the time-to-value of AI investments.
Efficiency
High storage performance is an important aspect of any DGX SuperPOD environment. But by focusing solely on extracting the greatest possible performance from SSDs, many organizations end up with small, concentrated, and expensive flash storage environments that don’t easily scale.
VAST Data prioritizes efficiency in its AI operating system — because at the end of the day, an AI data storage system needs to be able to scale affordably to handle the growing demands of AI model development. VAST provides an efficient, large-scale flash system with parallel-access storage, supporting in-place computation and eliminating the need to move data back and forth from archives. This intelligent data management enables true linear scaling for AI initiatives.
Simplicity
Another common characteristic that throws off the balance of AI data storage systems is unnecessary complexity — both in terms of the system’s design and how it’s accessed by users. In systems with many independent components, there can be performance hotspots due to the variable rates at which servers and SSDs access and store data. In addition to this phenomenon of stranded performance, traditional parallel file systems often require multiple management consoles for access and maintenance.
The VAST AI OS eliminates performance hotspots without the need for regular rebalancing, ensuring a consistent and balanced data flow across all servers for high-speed compute times. The platform also offers a single login portal that provides operational simplicity and non-disruptive system changes.
Avoid SuperPOD Stall-Out with VAST Data
Balanced data architectures drive efficiency and innovation without spiraling costs, and are the key to unlocking the full potential of NVIDIA DGX and SuperPOD deployments. Enterprises that invest in AI-native data storage alongside their AI development platforms are seeing higher GPU utilization, better model throughput, and faster iteration cycles.
If you want to understand why a purpose-built AI operating system is essential for success — and how to avoid common pitfalls along the way — check out our exclusive conversation with VAST Data’s Jeff Denworth and Jan Heichler, joined by The Register’s James Hayes. In the video, they dive into how scalable SuperPOD solutions can accelerate your AI journey and help integrate AI smoothly into your business.
VAST Data is dedicated to eliminating AI storage bottlenecks with a disaggregated, all-flash architecture that supports high concurrency and limitless scalability. Learn more about how VAST and NVIDIA are building balanced AI infrastructure for the next generation of AI workloads.
