As manufacturers continue adopting Industry 4.0 technologies, many are moving more data processing closer to the factory floor while keeping sensitive operational information on site. One manufacturing environment needed a way to help operators review PLC alarms, inspection camera results, and maintenance records through a simple local interface so technicians could quickly check machine conditions and maintenance history without relying on cloud connectivity. To support this requirement, Premio’s 1U Edge AI Server for On-Prem LLM Workloads, LLM-1U-RPL, was deployed to provide localized processing that helped teams identify production issues faster and maintain more consistent troubleshooting across shifts.
Challenges
- Need to run LLM inference locally so operators could review operational data and engineering documentation without sending out sensitive information to the cloud
- Existing factory floor systems were not designed to support GPU accelerated workloads for real-time analysis
- Difficulty bringing together operational data from multiple factory systems into a single troubleshooting interface
- Limited rack space inside control cabinets required a compact short depth rackmount solution
- Continuous production environments required hardware capable of secure and reliable 24/7 operation
Solution
- Premio’s 1U Edge AI Server for On-Prem LLM Workloads (LLM-1U-RPL)
- PCIe Gen 4 expansion supporting integration of an NVIDIA RTX PRO 4500 Blackwell GPU for accelerated LLM processing
- Multiple 2.5GbE LAN ports with USB and COM interfaces supporting integration with existing factory systems
- Compact rackmount form factor suitable for control rooms and factory infrastructure
- Redundant 600W power supplies with hot swappable fans and TPM 2.0 supporting secure 24/7 operation
Benefits
- Ten-year lifecycle support with Intel embedded processors
- Fast Deployment Within 4 to 5 Weeks
- NDAA and TAA compliant solution backed by Premio’s Los Angeles–based support team
Company Overview
The company runs automated production lines where inspection systems continuously check product quality and help operators detect issues quickly. As more machine data and technical documentation became available across the factory, operators needed a faster way to review this information while responding to problems during active shifts. As part of its Industry 4.0 initiatives, the company introduced localized edge computing to make troubleshooting information easier to access while keeping sensitive production data on site.
The Challenges
Need for Local LLM Inference
During production shifts, operators often needed to check machine data and engineering documentation while diagnosing equipment issues. Accessing this information through cloud connected systems could slow response time and raised concerns about keeping sensitive operational data off site. To address this, the team looked for a way to run LLM inference locally so troubleshooting guidance could be available directly within the factory environment.
Limited Infrastructure for GPU Accelerated Workloads
Most existing factory floor systems were designed for control and monitoring rather than running AI workloads. Attempting to process LLM tasks on the same infrastructure reduced system responsiveness and limited their ability to support real time analysis. The team therefore required a dedicated platform that could handle GPU accelerated processing without affecting normal operations.
Difficulty Accessing Operational Data Across Systems
Machine information, inspection results, and maintenance records were spread across several factory systems. When issues occurred, operators often had to check multiple sources before identifying the root cause. This made troubleshooting slower and led to inconsistent responses between teams and across shifts.
Space Constraints Inside Control Cabinets
Rack space inside control rooms and equipment cabinets was already limited, which made it difficult to install standard depth servers near production systems. As a result, expanding infrastructure to accommodate larger hardware would have increased deployment complexity and disrupted existing layouts. The team therefore needed a compact rackmount platform that could fit within the available space and integrate more easily into the current setup.
Requirement for Secure and Reliable Continuous Operation
Production systems run continuously, so any new hardware had to operate reliably without interrupting daily operations. The edge platform also needed to handle long running workloads while keeping sensitive factory data secure. For this reason, the team looked for a solution designed to support stable 24/7 operation in an industrial environment.
The Solution
Premio’s short depth 1U edge AI rackmount server (LLM-1U-RPL)
After reviewing the requirements for running LLM workloads directly inside the factory, the team selected Premio’s 1U Edge AI Server for On-Prem LLM Workloads (LLM-1U-RPL) as the foundation for the deployment. Its short-depth 1U design made it easier to install within existing control room racks, while the 13th Gen Intel Core processor and support for up to 64GB of DDR4 memory provided the performance needed to handle localized inference tasks. With additional support for GPU acceleration, PCIe expansion, and built-in reliability and security features, the LLM-1U-RPL fit naturally into the production environment without requiring changes to existing infrastructure.
GPU Accelerated LLM Processing
Running LLM workloads inside the factory required more processing capability than existing control systems could provide. Through PCIe Gen 4 expansion, Premio’s LLM-1U-RPL supports integration of workstation-class GPUs up to the NVIDIA RTX PRO 4500 Blackwell, allowing the team to accelerate local inference as requirements increased. This made it easier to review operational data and engineering documentation on site without depending on external compute resources.
Flexible Connectivity with Factory Systems
The deployment also required reliable access to existing factory systems where machine information and inspection data were already collected. Multiple 2.5GbE LAN ports, along with USB and COM interfaces, allowed the solution to integrate with these plant networks without additional interface hardware. As a result, the server could be introduced into the existing environment without changes to the factory’s network setup.
Compact Rackmount Deployment
Because installation space inside control rooms and infrastructure cabinets was limited, hardware size was an important consideration during deployment planning. With a short-depth 1U rackmount design measuring 483 (W) × 480 (D) × 44 (H) mm, the LLM-1U-RPL fit easily into existing racks without changes to the surrounding setup. Its compact footprint also allowed placement closer to production networks where operational data was already available.
Reliable and Secure Continuous Operation
Since production systems run continuously, the deployment required hardware that could operate reliably without interruption. Redundant 600W power supplies allowed maintenance or replacement without shutting the system down, and hot-swappable fans helped maintain stable performance during extended workloads. To further support operation in shared factory environments, a lockable front bezel, chassis intrusion detection, and TPM 2.0 provided additional protection for sensitive operational data at the edge.
The Benefits
Long Lifecycle Processor Support
The 13th Gen Intel Core processors used in the LLM-1U-RPL are part of Intel’s embedded roadmap with up to a ten-year lifecycle, helping ensure long term availability for maintenance planning and future upgrades.
Faster Deployment for Time-Sensitive Production Environments
Unlike larger server vendors that often involve longer procurement timelines, the LLM-1U-RPL supported deployment within approximately 4 to 5 weeks, allowing the team to introduce localized LLM inference quickly without delaying production support improvements.
Compliance Ready with Local Support
NDAA and TAA compliance supported deployment in environments with stricter sourcing requirements. Premio’s products are manufactured in Taiwan and assembled in the United States, helping meet supply chain expectations for regulated projects. The Los Angeles–based support team also provided a local contact for integration and ongoing assistance during deployment.
Conclusion
Running LLM workloads directly at the factory edge helped operators review production issues more quickly and respond with greater consistency across shifts. The LLM-1U-RPL supported this approach by fitting into existing rack infrastructure without requiring changes to the surrounding setup while keeping processing on site. Together, this shows how localized LLM inference can support everyday troubleshooting workflows as part of broader Industry 4.0 adoption. For more information about the LLM-1U-RPL, contact a product expert at sales@premioinc.com.