Dell has unveiled a strategic blueprint for AI networking and data center infrastructure solutions.

Scott Bils, vice president of product management, professional services at Dell Technologies Inc., emphasized that "the key to driving outcomes and business value from gen AI is data," with AI networking playing a critical role in this equation. In interviews with theCUBE Research's Rob Strechay at SC24, Bils explained that AI clusters require fundamentally different networking architectures than traditional data centers. "When you think about clusters of GPUs, you essentially want the clusters at a rack level, or even a data center level, to function as a single computer … a single brain," he said. AI workloads demand low latency, high throughput and seamless GPU-to-GPU communication, unlike conventional setups where data is stored and retrieved in silos.

To achieve integrated architectures that enable data centers to function as cohesive units, technologies such as InfiniBand and RDMA are becoming essential for connecting GPUs at scale. However, their complexity poses challenges for many organizations, particularly in terms of in-house expertise and architectural readiness. Recognizing this challenge, Bils noted that "as enterprise deployments begin to scale out, they're going to face and are facing similar issues. Helping them think through the overall design architecture, not just for today, but going forward as they scale out the environment, is a big part of the capability we bring — then, the expertise from Nvidia and our other partners in the space as well."

Dell's approach to addressing data management challenges involves automating and orchestrating data pipelines tailored to specific use cases. "It's helping them then integrate the data into the appropriate use cases and then automate and orchestrate that to ensure that you have the right velocity, including the right access to the right data sets to support the use cases," Bils said. He emphasized the comprehensive scope required, stating "it's also that life cycle view from identifying the data sources, classifying, curating, cleansing and then automation, ingestion and scaling. It's what organizations are going to have to do comprehensively to enable the AI opportunity." Critical to this is the implementation of AI-specific data catalogs that enhance data discoverability, classification and compliance, while tracking data lineage to ensure traceability and data integrity. "It gets back to the data quality issues, being able to track that lineage and who's touched that data," Bils explained, noting the importance of metadata about both the content and its transformations.

The integration of AI into data centers has escalated energy demands significantly, with GPUs driving substantially higher power consumption than traditional CPUs. Organizations face compounding pressures from geopolitical instability and infrastructure limitations while managing increasing regulatory pressure for sustainability. According to Bils, "when you take a look at your typical data center, 40% to 60% of the operating costs are driven by energy costs. A lot of the factors that drive prices there are beyond our customer's control: geopolitical factors, factors around infrastructure, brittleness and stability. They have to control what they can control from an energy and sustainability standpoint."