Dell Technologies today made a raft of additions to its infrastructure portfolio for building and deploying artificial intelligence (AI) applications and agents, including a data orchestration engine that automatically discovers, labels, enriches and transforms data into formats that AI applications can more easily access and consume at scale.
Announced at the NVIDIA GTC 2026 conference, Dell also revealed it has added support for the Blackwell series of graphics processing units (GPUs) developed by NVIDIA along with support for NVIDIA Cuda-Q, an extension to NVQLink for networking quantum and classical computing systems.
The latest series of servers are specifically designed for traditional enterprise and edge computing use cases involving AI applications, says Varun Chhabra, senior vice president of infrastructure (ISG) and telecom marketing at Dell.
Dell is also adding support for the latest Dell NVIDIA AI-Q blueprint for integrating data and the NVIDIA STX, a modular reference design for NVIDIA Vera Rubin NVL72, NVIDIA BlueField-4 DPUs, and NVIDIA Spectrum-X Ethernet networks across its server and network switch portfolio.
Finally, Dell has added an AI Assistant within the Dell Data Analytics Engine that brings a conversational natural language interface directly into analytics applications based on SQL.
Dell has been making a case for a Dell AI Factory portfolio of platforms it initially launched two years ago. Since then Dell claims to now have more than 4,000 organizations that have acquired a Dell AI Factory system.
The data orchestration platform extends the scope and reach of that no-code, low-code platform using a Dataloop platform that Dell acquired late last year. Additionally, Dell is launching a Data Orchestration Engine Marketplace through which organizations can access a set of workflows that were created using the NVIDIA NIM microservices framework along with NVIDIA AI Blueprints and more than 200 AI models, applications and templates.
The overall goal is to make it simpler to expose the highest quality data possible to AI models at the right time, says Chhabra. “Data availability and quality is the most common challenge,” he says.
It’s not clear to what degree organizations are deploying AI applications in the cloud or in an on-premises IT environment. However, a recent Futurum Group survey finds cloud deployments account for 35% of implementations, compared to on-premise and private cloud deployments that have a 24% share and hybrid IT environments that have a 21% share. In fact, it’s not uncommon for organizations to build an AI application in the cloud that is later deployed closer to the point where data is being created, analyzed and consumed.
Additionally, many organizations also have to address compliance and security concerns that might require an AI model to be deployed in an on-premises IT environment.
Regardless of where any AI model ultimately is deployed, the one thing that is certain is that in time IT teams will not only be expected to ensure they are consuming the right data, but also be replaced as often as needed as the pace of AI innovation only continues to accelerate.
