Dell’s AI Data Platform will use consistent data item classifications and relationships to select and serve better data to Nvidia cuDF accelerated data prep compute for faster delivery to GPUs for AI processing.
The company says AI models and agents need access to the right data from a disparate and distributed data estate with multiple different silos and data formats widely spread across an enterprise. Its improving the PowerScale file storage applicability to that data estate by increasing its tenant capability to 500 tenants per cluster and also providing a fully-managed, cloud-native version of PowerScale for Microsoft’s Azure public cloud. The multi-tenancy features mTLS over NFS to encrypt and authenticate file traffic and granular role-based access control for managing each tenant.
Dell Infrastructure Solutions Group President Arthur Lewis stated: "Data without context is just noise. Most enterprises have spent years making their data accessible. That's not the same as making it usable. An agent that can find a customer record but doesn't know what it means, how it connects to everything else, or whether it can be trusted isn't intelligent. It’s just fast. The companies that solve it won't just deploy AI faster or run more agents. They'll get more out of the data they already have.”
The company is announcing a Unified Semantic Layer, Enterprise Knowledge Graphs and subject matter-specific Enterprise AI Agents to identify and collect he most appropriate and consistent data for an AI task. Specifically;
- Unified Semantic Layer - gives structured and unstructured information consistent business meaning with rules, definitions and a searchable glossary, so a term means the same thing everywhere it appears. If one system calls it a client and another calls it an account, AI now knows they're the same thing. Users can also import existing ontologies and classification taxonomies, reusing industry-standard or enterprise-specific taxonomy assets. Dell is also enabling Nvidia Auto-Ontology, an open-source library that builds knowledge graphs from enterprise data, to extend the Unified Semantic Layer.
- Enterprise Knowledge Graph - maps how structured and unstructured data is related, so an application or an agent can find the right context. It uses metadata, lineage and query history to keep tuning the graph as activity changes. When an agent asks a question, the platform pulls in every related piece it's allowed to see: the right tables, data products, multimodal data and vector indexes, wherever they live. For instance, a manufacturer chasing a production line problem can trace one odd sensor reading to the machine, its repair history, the supplier batch and the orders at risk.
- Knowledge Agents - act as a trusted advisor on a single topic, grounded in a defined slice of the Enterprise Knowledge Graph. Customers set the rules: what guidance it follows, what data it can see, what quality bar it must clear and how much it's allowed to spend. Nvidia Nemotron Retriever models provide reasoning and visual understanding to Knowledge Agents.
Once data has been initially selected for AI processing then Dell is now offering cuDF accelerated data preparation with Apache Arrow. Nvidia cuDF is an open-source, GPU-accelerated library for processing tabular data, built on Apache Arrow’s columnar memory format and optimized CUDA kernels. Operations such as filtering, joins, group-bys, sorts, and aggregations can run across thousands of GPU cores at once, with less data movement between CPU and GPU.
Dell’s SVP for Product Marketing, Varun Chhabra said: ”Nvidia cuDF accelerates data processing, GPU -accelerated Apache Spark prepares structured and unstructured data at scale, and Apache Arrow moves data efficiently between data storage and processing.”
Dell says its Data Processing Engine, powered by cuDF on Nvidia RTX PRO 4500 Blackwell Server Edition GPUs, processes data nearly 4 times faster on average than CPUs alone across a range of workloads, and up to 20 times faster on batch processing workloads.
A new Dell Storage Performance Tool helps customers size AI infrastructure, compare vendor solutions and keep GPUs fed by testing the performance of their S3-compatible object storage across training, inference and checkpointing. Chhabra said: “It is an open-source tool for Dell Object Scale and Dell PowerScale that measures object storage under realistic repeatable AI workload capabilities. Organisations can define the workload they want to benchmark our storage platforms with. And then [test] checkpoint-style writes, high concurrency reads, mixed read write environments, even Iceberg queries.”
“They can all run these benchmarks on their own infrastructure with the same tool that Dell Engineering is using internally for benchmarking our products. It measures throughput and latency live and verifies persistent data end-to-end, and every result records versions and provenance, so it's a repeatable process.”
Dell Professional Services are being expanded with AI Data Platform implementation services across its data and storage engines. The aim is to establish a production-ready foundation, activating the platform’s analytics, processing, search and orchestration capabilities while keeping the AI Data Platform system tuned for the best performance.
Availability
- The Unified Semantic Layer, Enterprise Knowledge Graph and Knowledge Agents will be released in 1H 2027.
- Dell Data Processing Engine enhancements with the Nvidia acceleration stack will be available in December 2026, with further acceleration using Apache Arrow expected 1H 2027.
- Dell PowerScale security and multi-tenancy enhancements will be available in November 2026.
- The cloud-native Azure PowerScale service is available now, starting with a US East region.
- The Dell Storage Performance Tool is available from GitHub.
Bootnote
Dell says its fully-managed, cloud-native PowerScale on Azure “provides 4x faster performance than our closest competitor, 4x larger namespace, as well as a 2x higher cluster resiliency than our closest competitor.” We understand the closest competitor is Qumulo’s Azure Native Qumulo (ANQ).