Many organizations today are turning to cutting-edge NVIDIA DGX SuperPODs to perform in-house AI development. But by still relying on legacy data infrastructures to feed these modern AI training systems, are they truly maximizing their NVIDIA DGX investments?
This article will cover the ins and outs of a DGX SuperPOD, the importance of the right data storage for AI innovation, and how NVIDIA and VAST Data systems work together to set up enterprises for AI success.
What is a SuperPOD?
NVIDIA is a leader in AI computing, and their DGX SuperPOD infrastructure is one of the company’s latest advancements that’s helping enterprises power AI factories to handle the most challenging AI workloads. Before digging deeper into the specifics of the SuperPOD, though, let’s look at the NVIDIA DGX system at a broader level.
Inside the NVIDIA DGX System
The NVIDIA DGX platform is a fully-integrated computing system designed for large-scale AI training and inference. Each NVIDIA DGX server is a self-contained powerhouse, built specifically for AI and high-performance computing (HPC) workloads. DGX servers provide an ideal platform for training deep neural networks, and feature:
8x NVIDIA H100 or B200 Tensor Core GPUs
High-bandwidth memory and NVLink interconnects
NVIDIA software stack (CUDA, cuDNN, TensorRT, etc.)
For enterprise organizations needing even more AI development capabilities, a DGX SuperPOD is the next level up — it’s NVIDIA’s blueprint for building a scalable AI supercomputer.
The Role of the DGX SuperPOD
Comprising dozens (or even hundreds) of interconnected NVIDIA DGX systems, a SuperPOD operates as a single massive AI supercomputer — capable of handling petabyte-scale datasets and powering some of the largest AI models in the world. DGX SuperPODs deliver unmatched computational performance for enterprises that are developing and training large language models (LLMs), computer vision algorithms, and other advanced AI workloads.
The DGX SuperPOD infrastructure combines:
Multiple NVIDIA DGX H100 or DGX A100 systems
High-speed NVIDIA InfiniBand networking
Advanced software like NVIDIA Base Command
An AI-optimized storage layer (such as VAST Data)
The Hidden AI Bottleneck: Data Storage
While NVIDIA DGX systems are capable of processing data at incredible speeds, their performance is only as good as the data pipeline feeding them. AI performance improvement efforts tend to focus on adding or optimizing GPUs, but an organization’s data storage infrastructure is a critical component in determining whether or not an AI cluster runs efficiently.
The problem is that traditional data storage solutions simply can’t keep up with the high data volumes and speed required for true AI innovation.
Why Does Storage Matter in AI Pipelines?
The data storage requirements for AI are unique, so a data pipeline set up for AI development needs to be able to handle much greater data intensity and velocity. Here’s why data storage matters so much for AI, and where standard data infrastructures fall short:
Input/Output Heavy
AI workloads are data-intensive, and datasets must be continuously streamed to GPUs at high speeds to fuel effective model training. The high latency of traditional data infrastructures results in inadequate data throughput, leading to idle GPUs, slow model training, and longer time-to-insight.
Parallel Processing
The data pipeline must support thousands of parallel jobs — without bottlenecks — including random, concurrent reads from large datasets. The sequential nature of many legacy data systems throttles model development and significantly limits AI scalability for enterprises.
Detailed Record-Keeping
Model checkpoints, logs, artifacts, and outcomes must be written back frequently and consistently for model improvement and compliance assurance. Standard data infrastructures either can’t appropriately capture the required AI development log data, or struggle to handle the volume of decision data being fed back into the training phase.
Storage Requirements for DGX SuperPOD Environments
For AI systems to operate at peak efficiency in the NVIDIA DGX SuperPOD environment, the data storage layer needs to meet some specific architectural requirements:
High Throughput: Storage system bandwidth capable of feeding petabytes of data to GPUs across hundreds of nodes.
Low Latency: Instant availability of data, especially during AI model training and real-time inferencing.
Scalability: Ability to scale as models grow and data multiplies, without requiring complex re-architectures.
Parallel Access: Capable of seamlessly and concurrently handling the thousands of parallel file and object requests involved in an AI workload.
VAST Data + NVIDIA DGX: A SuperPOD-Ready Storage Solution
To maximize the NVIDIA DGX SuperPOD architecture, organizations need a modern data storage layer that can keep up. VAST Data’s AI-native data storage platform has been built specifically for this use case — ready to handle even the most rigorous AI workloads.
VAST Data delivers:
A high-performance data platform designed to feed data to AI workloads, especially the kind running on NVIDIA DGX systems and SuperPODs.
A disaggregated, scale-out architecture designed for exabyte-scale AI environments.
Unified file and object access to simplify data management across AI training and inference pipelines.
Support for GPUDirect Storage to minimize latency between GPU memory and storage.
The VAST AI Operating System (AI OS) works seamlessly with NVIDIA DGX environments. For example, an enterprise’s NVIDIA DGX SuperPOD can be thought of as the brain in the AI development operation, performing the heavy computational tasks such as model training, inferencing, and simulation. Working in unison with this system, the VAST AI OS is the central nervous system, making sure the brain receives a constant flow of data quickly, reliably, and at scale.
Next Steps: Future-Proofing SuperPOD Architecture
To stay competitive in today’s fast-paced marketplace, organizations are building robust data infrastructures capable of handling their AI development needs for years to come. And they’re recognizing that AI-native data storage is a hugely important part of that process, because:
GPUs alone won’t unlock AI scale; data architecture and storage must keep pace.
High-performance, parallel storage is essential to avoid the risk of underutilizing your NVIDIA DGX systems.
Future-proofing your AI investments involves choosing data storage that scales effortlessly and limitlessly.
Whether your organization is running a single NVIDIA DGX system, or deploying a full NVIDIA DGX SuperPOD with dozens or hundreds of DGX nodes, VAST Data makes sure your storage infrastructure supports your AI innovation for years to come.
Watch this webinar to discover the 12 reasons why your AI initiatives - supported by the NVIDIA DGX SuperPOD - need VAST’s purpose-built AI OS.
