Your GPUs Are Ready. Is Your Data?

Key takeaways 11 min read

The AI bottleneck has moved from compute to data: GPU investments stall when data is fragmented, not transformed to curated knowledge agents and models can understand, or is difficult to operationalize.
The Dell AI Data Platform unifies AI storage, intelligent data engines and orchestration — with security and governance embedded throughout — to move organizations from raw data to AI outcomes.
Three storage engines (PowerScale, Lightning File System, ObjectScale) each serve a distinct stage of the AI lifecycle, so the right tool is always matched to the right workload.
GPU acceleration is integrated directly into the data processing layers via NVIDIA CUDA-X libraries, ensuring data is processed most efficiently and at highest performance. Storage engines maximize GPU efficiency by delivering data at the rate GPUs can consume it.
An open, modular architecture enables ecosystem integrations and ensures enterprises adopt the best new technologies to make data AI ready — without being locked into a single vendor roadmap.

If your enterprise has the GPU infrastructure in place but AI initiatives keep stalling, the problem almost certainly isn’t the compute. So what is it?

The constraint has quietly shifted. For most enterprises in 2026, the bottleneck is no longer compute processing— it’s the data feeding it. Walk into almost any enterprise AI conversation today and it starts the same way: which models, how many GPUs, which accelerators. Those are the right things to be excited about. But they’re no longer what determines success.

The organizations realizing the greatest value from AI aren’t simply deploying more compute — they’re turning enterprise data into actionable AI outcomes faster and at greater scale. The most expensive infrastructure in the building can only deliver outcomes as good as the quality of data driving reasoning and action.

This is the challenge the Dell AI Data Platform was built to solve.

Three barriers standing between enterprises and AI at scale

Many organizations face the same three obstacles on the path to driving AI outcomes at scale. Naming them clearly is the first step to solving them.

Fragmented Across Silos: Customer data, product data, machine data, documents, images, and video are scattered across systems that don’t communicate — making it difficult for AI to access trusted information when and where it’s needed.
Not AI Ready: Most enterprise data isn’t AI-ready. It must be searched, analyzed, enriched, and transformed before AI agents and models can generate meaningful results. IDC estimates that over 80% of enterprise data is unstructured — and most of it is invisible to AI systems today. To accelerate enterprise business processes, both unstructured and structured data needs to be AI ready and accessible to agents. Unstructured data can be many different modalities with each requiring processing specific to that data type.
Stuck in AI Experimentation Even when data is available and prepared, organizations often struggle to operationalize AI across teams, workflows, and environments — leaving GPU clusters underutilized and business value on the table.

The Dell AI Data Platform addresses all three through a unified AI data stack that combines AI storage, intelligent data engines and data orchestration — with security and governance embedded across the software and infrastructure layers.

The shift: from storage infrastructure to an AI data stack

For decades, the data conversation was largely a storage conversation — capacity, performance tiers, and storage access protocols. That model breaks down the moment AI enters the enterprise discussion.

Training, RAG, and agentic workflows don’t simply store data. They continuously access it, transform it, search it, enrich it, and deliver it back at machine speed. Instead of forcing traditional storage architectures to support these new demands, the Dell AI Data Platform brings together AI storage, data engines and orchestration in a single stack designed to move organizations from raw business data to intelligent outcomes.

Here’s how each layer contributes.

AI storage: the right engine for every stage of the AI lifecycle

The first challenge is ensuring AI can access the right data at the right time with right performance. But here’s the part most “AI storage” pitches gloss over: there is no single storage system right for every AI workload. The economics and physics don’t allow it. Using extreme-performance flash for bulk archival data is wasteful; using general-purpose NAS to feed thousands of training GPUs simply can’t keep up.

Rather than force one system to do everything, the Dell AI Data Platform provides three storage engines — each optimized for a different stage of the AI lifecycle:

The principle underneath all three is the same: storage exists to store and deliver data at the speed of the GPUs, not the other way around. It maximizes GPU efficiency during inference leading to 19x faster time to first token and 5x higher token throughput. With up to 150 GB/s of throughput per rack unit, the platform is engineered to keep data moving fast to feed modern AI pipelines instead of starving expensive compute resources.

Data engines: from raw enterprise data to AI-ready intelligence

Access to data alone isn’t enough. Enterprise data must be transformed into AI-ready intelligence before models can generate meaningful results. It’s structured and unstructured. It lives across different systems. It must be queried, processed, searched, indexed and embedded before AI can do anything useful with it.

That’s the role of the data engines — best-of-breed capabilities that sit above storage and make data usable without forcing organizations to centralize it first:

Federated SQL, powered by Starburst — enables organizations to query heterogeneous data sources in place, analyzing data where it lives instead of creating yet another copy. The principle: bring the work to the data, not the data to the work.
Highly-scalable processing engine, powered by Apache Spark — transform and prepare data at the volume and speed the modern AI demands, handling the heavy lifting of feature engineering and AI pipeline preparation.
Unstructured search, powered by Elastic — makes the 80%+ of enterprise data, that isn’t stored in a database, discoverable and usable for RAG and agentic applications.

What makes this more than a familiar data stack is where the acceleration happens. Dell pushes GPU acceleration directly into the data processing layers at the most optimal place in the data flow. NVIDIA CUDA-X libraries — including cuDF for structured columnar processing and cuVS for vector indexing — are integrated in the SQL query processing, spark data frames, and vector indexing / search. The result is measurable: organizations can achieve up to 6x faster SQL queries, and 12x faster vector indexing, enabling prepared data to arrive at the rate GPUs can consume it. The data engines deliver highly scalable performance by making best use of CPU and GPU compute processing, while data flows directly between storage and GPU memory. NVIDIA RTX PRO Blackwell GPUs are integrated in the data platform layer to process structured and unstructured data at scale. The result: prepared and indexed data arrives at the rate GPUs can consume it and is processed using a combination of CPUs and GPUs based on the data processing operation. The tasks that can be parallelized are executed by the GPUs, while CPUs process other complex operations to deliver fastest job completion at lowest cost.

What this looks like in production

The platform’s value isn’t theoretical — it’s visible in how enterprises are actually deploying it today.

CTBC Bank built an AI-powered fraud prevention capability on the Dell AI Data Platform, processing every transaction in real time against more than 400 risk factors. The result: a fraud detection response time of just 0.03 seconds and a 40% reduction in fraud losses year over year.

Dell’s own analytics team eliminated ETL pipelines by querying data in place across a multi-petabyte data lake in ObjectScale — achieving a 3x boost in query performance, 30x compute efficiency, and consistent sub-second response times on datasets exceeding 200TB.

NTT DATA used the platform to stand up a Sovereign AI Factory initiative for enterprise clients, processing massive data volumes in minutes instead of hours — highlighted at the NVIDIA GTC 2026 keynote as a flagship example of the Dell + NVIDIA + Starburst integration in action.

These aren’t proof-of-concept deployments. They’re production AI systems running at enterprise scale.

Data orchestration: what turns AI projects into AI outcomes

Storage and data engines solve critical challenges — but they don’t automatically create business outcomes. Data still must move through ingestion, preparation, retrieval, governance, model fine-tuning and inference workflows in a coordinated way.

This is where the Dell Data Orchestration Engine comes in. Fast components don’t automatically create a fast pipeline. The layer that turns data into AI ready knowledge and outcomes is the data orchestration — coordinating the flow from ingestion through preparation, retrieval, and inference so data moves as a unified pipeline rather than a series of disconnected handoffs.

By automating and orchestrating data movement and AI workflows across environments, the platform helps organizations:

Reduce manual effort in moving and preparing data
Accelerate deployment of AI workloads into production
Move AI initiatives out of experimentation and into repeatable, scalable outcomes

In other words, orchestration is what transforms a collection of powerful technologies into an operational AI system.

Open and modular — by design

There’s a tempting shortcut in this market: buy a single, closed AI stack and let the vendor make every decision for you. It’s simpler on day one and a trap by day one hundred.

The Dell AI Data Platform takes the opposite approach:

Open table and file formats — no proprietary lock-in
Best-of-breed data and storage engines you choose — not a single engine chosen for you
Flexible ecosystem integrations – across inference, training and agentic frameworks
Modular building blocks — start small, scale data processing and storage layers independently
Bring AI to data anywhere — across core, cloud, and edge without forcing migration

That openness isn’t a checkbox. It’s what allows enterprises to adopt the best new engine or framework as it emerges rather than waiting on a vendor roadmap — and what keeps organizations in control of their own data, architecture and future.

Your GPUs Are Ready. Here’s How to Make Sure Your Data Is Too.

The next leap in enterprise AI won’t be decided by which organization has the most compute. It will be decided by which organization can turn its enterprise data into actionable AI outcomes — faster, at greater scale, and with governance built in from the start.

The Dell AI Data Platform is built for exactly that.

FREQUENTLY ASKED QUESTIONS

Why do GPU investments stall even in well-funded enterprise AI programs? The most common reason is data fragmentation. GPU clusters are powerful, but they’re only as productive as the data supplied to them. When data is scattered across silos, locked in structured and unstructured data silos, or difficult to operationalize at scale, even the most advanced compute infrastructure sits underutilized. The Dell AI Data Platform is specifically designed to close this gap.

What’s the difference between the three Dell storage engines? Each engine is optimized for a different point in the AI lifecycle. PowerScale handles the broadest range of AI workloads — ingestion, curation, training, and inference — with enterprise NAS simplicity. Lightning File System is built for extreme-scale parallel training and inference, where thousands of GPUs need continuous, high-throughput data supply. ObjectScale is purpose-built for massive unstructured datasets and data lakes. The right choice depends on workload, scale, and performance requirements — and you can use all three.

What does “query in place” actually mean, and why does it matter? Federated SQL, powered by Starburst, enables AI applications to query data across heterogeneous systems without first copying it into a centralized warehouse or data lake. This eliminates slow, expensive ETL pipelines, reduces data duplication, and simplifies governance — because there’s only one copy of the data, and it stays where it lives.

How does the platform handle unstructured data? Unstructured search, powered by Elastic, makes the 80%+ of enterprise data that lives outside structured databases — documents, emails, logs, images, video — discoverable and usable for RAG pipelines and agentic AI applications. This turns previously invisible enterprise data into an active AI asset.

Is this platform locked to Dell infrastructure? No. The platform is built on open table and file formats, with modular, best-of-breed engines. It supports deployment across core, cloud, and edge environments and is designed to evolve as new frameworks and engines emerge — without requiring a full-stack replacement.

Ready to see what your data can do?

→ Explore the Dell AI Data Platform

→ See how CTBC Bank cut fraud detection to 0.03 seconds with Dell

→ Read the ESG Economic Validation: 45% lower TCO, 414% ROI

→ Talk to a Dell AI specialist

References

Data Center