The Industrial Edge AI approach marks a major shift from traditional cloud-dependent generative AI by running fully on local hardware such as HMI panels, industrial appliances, engineering desktops, and on-premise or private cloud servers.
This local-first architecture delivers instant reasoning and parallel processing without relying on internet connectivity. It fits environments where data must never leave the premises, including regulated factories, power plants, and critical infrastructure sites that cannot risk cloud exposure or network outages.
Fine-tuning plays a central role in the effectiveness of the Industrial Edge AI system. Rockwell customizes NVIDIA’s Nemotron Nano small language model using FactoryTalk Design Studio Copilot data, making the model fluent in the terminology and workflows of industrial automation.
This tailored training allows the AI to interpret engineering diagrams, equipment configurations, and operational constraints that generic models fail to understand. The outcome is an assistant built for automation professionals rather than a one-size-fits-all chatbot.
Tony Carrara, FactoryTalk Design Studio Business Manager at Rockwell Automation, highlighted the need for AI that behaves predictably inside secure environments. He explained that fine-tuning the open Nemotron model with domain-specific data enables deployment anywhere without compromising control.
This reliability matters because industrial AI recommendations directly influence physical machinery. Small language models offer the determinism and stability engineers require, reducing the unpredictability that larger models sometimes introduce.
Data security remains a defining advantage of the Industrial Edge AI framework. Many manufacturers cannot use cloud-based AI due to data sovereignty requirements, export controls, or cybersecurity regulations mandating that production systems remain isolated.
By processing all operational data locally, the edge architecture fulfills strict compliance needs while still delivering the benefits of generative AI. Sensitive production data never travels outside the facility, eliminating a major blocker that has slowed AI adoption in high-risk sectors.
Early testing reveals that the Industrial Edge AI implementation provides significant advancements in reasoning speed, parallel task handling, and overall performance. These improvements allow the small language model to excel in use cases where immediate responsiveness matters. Large cloud-based models may offer extensive capabilities, but they cannot match the instantaneous decision support required on factory floors, where milliseconds can influence safety, efficiency, and equipment health.
Joey Conway, Senior Director of Generative AI Software for Enterprise at NVIDIA, positioned this collaboration as part of a broader movement toward real-time intelligence at the source of operations. He noted that small language models like Nemotron Nano bring AI directly to environments with limited power, space, and connectivity. This ability allows manufacturers to integrate advanced reasoning into machines, production lines, and energy systems without relying on data center infrastructure.
The Industrial Edge AI approach supports end-to-end workflows across design, development, production, and maintenance. Engineers can use AI during system design to generate logic suggestions or documentation, while technicians can later use the same assistant to diagnose issues, interpret error codes, or identify corrective actions. This continuity reduces the fragmentation seen in many industrial tech stacks and helps teams maintain consistent knowledge across the equipment lifecycle.
Flexibility in deployment makes the solution accessible to both small factories and global enterprises. Running efficiently on devices with limited compute resources means organizations do not need extensive IT investments to adopt generative AI.
Even facilities with modest infrastructure can introduce AI-powered decision support, lowering barriers for manufacturers that previously lacked the resources to deploy advanced automation technologies. This democratization could accelerate digital transformation across the industrial sector.
Looking forward, Rockwell plans to highlight the Industrial Edge AI system at Automation Fair 2025 in Chicago from November 17–20. The demonstration will showcase how edge-based generative AI is reshaping the future of industrial operations.
Industrial Edge AI Boosts Rockwell’s Factory Performance
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