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Published
18 Jul 2025Form Number
LP2260PDF size
42 pages, 1.6 MBSubscribed to LP2260.
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Abstract
This Reference Architecture presents Lenovo’s end-to-end approach for deploying AI inference workloads at the edge using the ThinkEdge server portfolio. With the exponential growth of data generated at the edge from sensors, cameras, and IoT devices, Lenovo provides a flexible, scalable, and secure infrastructure designed to support real-time AI decision-making.
This architecture supports a diverse set of verticals—including retail, manufacturing, healthcare, financial services, and smart cities. The document outlines nine pre-validated server configurations, ranging from X-Small to X-Large, optimized for workloads such as computer vision, NLP, environmental audio intelligence, and LLM/VLM inference. It highlights key technologies like vLLM, TensorRT-LLM, and OpenVINO for optimized AI performance across Intel and NVIDIA-based systems.
The architecture supports hybrid AI strategies, allowing edge inference while integrating with centralized training and governance systems. Lifecycle management tools such as Lenovo XClarity One and LOC-A streamline deployment, monitoring, and security with zero-touch provisioning and multi-cloud orchestration. Through this architecture, Lenovo empowers organizations to unlock actionable insights at the edge, reduce latency, improve data sovereignty, and accelerate time to value for AI solutions.
Table of Contents
Introduction
Solution Overview
Edge Location in a Hybrid AI Platform
Lenovo ThinkEdge Server Portfolio
Workload-Optimized AI Hardware Guide
Test Overview
Test Results
Life Cycle Management Software
Summary
Appendix: Lenovo Bill of Materials
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