Dell AI Data Platform Advancements Help Customers Harness Data to Power Enterprise AI with NVIDIA ..

• 13 Aug 2025
• TM Team
• 0 Comments

Dell Technologies, the world’s No. 1 provider of AI infrastructure,1 today announced updates to the Dell AI Data Platform to help customers better support the full lifecycle of AI workloads from ingestion and transformation to agentic inferencing to AI-powered knowledge retrieval.

Why it matters

Enterprise data is massive, growing rapidly and increasingly unstructured, but only a fraction of it is usable for generative AI today. To unlock its value, organizations need continuous indexing and a vector retrieval engine that converts content into embeddings for fast, precise semantic search. As workloads grow, organizations need infrastructure that streamlines data preparation, unifies data access across silos and delivers end-to-end enterprise-grade performance.

The latest updates to the Dell AI Data Platform enhance unstructured data ingestion, transformation, retrieval and compute performance to streamline AI development and deployment – turning massive datasets into reliable, high quality real-time intelligence for generative AI.

Accelerating AI inferencing and analytics

The Dell AI Data Platform helps customers quickly move from AI experimentation to production by automating data preparation.

At the core of the Dell AI Data Platform’s architecture are specialized storage and data engines that help seamlessly connect AI agents to high quality enterprise data. Together, the Dell AI Data Platform and the NVIDIA AI Data Platform reference design provide a validated, GPU-accelerated solution that integrates storage engines and data engines with NVIDIA accelerated computing, networking and AI software to power generative AI systems.

Expanding the capabilities of the Dell AI Data Platform is the new unstructured data engine, designed to provide real-time, secure access to large-scale unstructured datasets for inferencing, analytics, and intelligent search. This engine, made possible through a new collaboration with open-source Search AI leader Elastic, will offer customers advanced vector search, semantic retrieval and hybrid keyword search capabilities—key capabilities for powering AI applications. Additionally, the unstructured data engine will leverage built-in GPU acceleration to deliver breakthrough performance.

The unstructured data engine works alongside the platform’s other tools, like a federated SQL engine for querying scattered structured data, a processing engine for handling large-scale data transformation, and storage designed for fast, AI-ready access.

Powering enterprise AI discovery

As AI becomes increasingly crucial for business-as-usual operations, Dell PowerEdge R7725 and R770 servers featuring NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs provide the mainstream computing foundation for accelerated enterprise workloads, from visual computing, data analytics and virtual workstations, to physical AI and agentic inference. These servers are ideal for running NVIDIA AI reasoning models such as the latest NVIDIA Nemotron models for agentic AI, as well as NVIDIA Cosmos world foundation models for physical AI.

Offering better price for performance for a wide range of enterprise use cases, these air-cooled systems make flexible high-density AI compute more attainable. The NVIDIA RTX PRO 6000 offers enterprises up to six times the token throughput for LLM inference,2 double the capacity for engineering simulation performance3 and can support four times the number of concurrent users compared to the previous generation with support for MIG.

The Dell PowerEdge R7725 server will also be the first 2U server platform to integrate the NVIDIA AI Data Platform reference design. When the Dell PowerEdge R7725 server featuring NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs is paired with the Dell AI Data Platform and its new unstructured data engine, enterprises can take advantage of a turnkey solution without the need to architect and test their own hardware and software platforms. The combination of the two delivers faster inferencing, more responsive semantic search and support for larger, more complex AI workloads.