Traditional retail analytics rely on sales/inventory data and manual checks, leaving huge blind spots in real-time shelf availability, promotional compliance, customer behavior, and store layout effectiveness. Edge AI fixes this by automatically processing in-store video on-site to deliver instant, actionable insights – turning every store into a responsive, data-driven operation that maximizes sales, cuts waste, and improves the shopper experience.
Reducing shrink (theft, mis-scans, errors, damage) is one of the fastest ways retailers can recover lost revenue, yet traditional loss-prevention methods are slow, reactive, and prone to false alarms. Edge AI changes this by analyzing video and POS data in real time at the store level, catching issues the moment they happen, distinguishing honest mistakes from intentional acts, and alerting staff discreetly – all while protecting customer privacy and experience.
Edge AI turns in-store cameras and sensors into real-time operational tools that – rather than replace employees – help them deliver faster service, fresher food, shorter queues, and better customer experiences in grocery, deli, convenience, and food-service retail environments.
Traditional retail analytics rely on sales/inventory data and manual checks, leaving huge blind spots in real-time shelf availability, promotional compliance, customer behavior, and store layout effectiveness. Edge AI fixes this by automatically processing in-store video on-site to deliver instant, actionable insights – turning every store into a responsive, data-driven operation that maximizes sales, cuts waste, and improves the shopper experience.
Reducing shrink (theft, mis-scans, errors, damage) is one of the fastest ways retailers can recover lost revenue, yet traditional loss-prevention methods are slow, reactive, and prone to false alarms. Edge AI changes this by analyzing video and POS data in real time at the store level, catching issues the moment they happen, distinguishing honest mistakes from intentional acts, and alerting staff discreetly – all while protecting customer privacy and experience.
Edge AI turns in-store cameras and sensors into real-time operational tools that – rather than replace employees – help them deliver faster service, fresher food, shorter queues, and better customer experiences in grocery, deli, convenience, and food-service retail environments.
As retailers operate thousands of geographically dispersed locations, centralized edge management is essential for real-time AI, low latency, operational continuity, and security. By placing powerful compute at the edge, retailers gain centralized control, automated updates, and resilience even during network outages.
Supermicro and NVIDIA are bringing enterprise-ready systems to the edge, enabling industries like retail, manufacturing, telecommunications, and smart spaces to run powerful AI inference closer to their data sources. With compact, efficient architectures and latest-generation GPU acceleration, these solutions transform how enterprises process and act on real-time information.
The telecommunications landscape is shifting as accelerated computing becomes mainstream. This transformation unlocks the potential to monetize the telco edge with AI applications, by offering GPU infrastructure to internal and 3rd party users while simultaneously running 5G/6G Radio-Area-Network (RAN) software. This unified approach is no longer a future concept – it's a critical business imperative for creating new revenue streams and monetizing the edge. But how can you build a platform that can generate new revenue while meeting the strict demands of RAN? Join experts from Supermicro and Aarna.ml for a deep dive into a comprehensive reference architecture for AI RAN distributed inference. We will unveil a complete, cloud-native solution designed to transform your edge sites into dynamic, monetizable AI platforms that can also run RAN software.
Supermicro and NVIDIA are transforming retail by delivering edge AI solutions that bring intelligence directly into the store.
As the adoption of AI use cases in retail, manufacturing, smart spaces, and other industries continues to expand, enterprise infrastructure performance at the edge needs to keep up. Finding the right balance between performance and TCO is vital for a successful and sustainable business case. Additionally, enterprises are embracing specialized AI models for predictive, generative, physical, and agentic AI, making low-latency data processing for real-time decision-making even more critical. Join us as we discuss Edge AI use cases to demonstrate how businesses can drive growth and operational excellence and how Supermicro's edge portfolio is designed to deliver the required AI performance at the edge.
Closer to Data, Ahead of Tomorrow’s Intelligence
Supermicro ARS-E103-JONX: Performance Optimized, Fanless System for AI at the Edge
Supermicro PCIe GPUs can be put into a data center, edge device, or even a high-end workstation.
ServeTheHome dives deep into Supermicro’s extensive system portfolio to understand how Supermicro systems with NVIDIA GPUs can be combined to deploy AI for almost any industry, anywhere from the data center to the edge.
Performance and Efficiency for Distributed AI Applications
Supermicro and Spectro Cloud deliver a comprehensive AI edge solution through pre-validated, optimized hardware and Spectro Cloud’s Palette EdgeAI platform, designed to simplify and scale the deployment and management of AI workloads across diverse edge environments.
Supermicro X14 Servers Give Users the Flexibility to Match Systems and Processors to Workloads
