Advantech AIR-411 — Compact 17 L Edge AI HPC System with 300 W GPU Support

Specifications

Overview

The Advantech AIR-411 is a compact edge AI HPC system that brings discrete-GPU inference into spaces that normally force a fanless, GPU-less industrial PC. It pairs Intel Core 200S (Bartlett Lake-S) processors with up to 24 cores and 32 threads against a single PCIe Gen4 x16 slot that accepts a full-height, full-length GPU drawing up to 300 W — which is enough headroom for the modern inference accelerators that industrial AOI and edge LLM workloads actually use.

The supported accelerator list is deliberately broad rather than NVIDIA-only: Intel Arc Pro B60, NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition, AMD Radeon PRO W7900 and W7800, and the Qualcomm Cloud AI 100 Ultra. That matters for customers with existing vendor preferences or supply constraints, and it means the same chassis can serve a CUDA pipeline and an OpenVINO or ROCm pipeline without a platform change.

The 17-litre chassis and sub-38 dB acoustics are what let the system sit on a factory floor or in a clinical room rather than in a server closet. Advantech pairs the hardware with its Edge AI SDK and pre-trained models, and with WEDA for centralized management — deploying validated models, managing AI workloads and pushing updates across many distributed AIR-411 nodes from one control plane. With up to 10 years of long-term support, the platform is intended to be deployed once and maintained for the life of the equipment it serves, which is the buying pattern in semiconductor inspection and medical imaging.

Key Benefits

300 W discrete GPU in a 17-litre box, bringing real inference throughput to space-constrained edge sites. Broad accelerator support across Intel, NVIDIA, AMD and Qualcomm so the chassis is not tied to one vendor ecosystem. Sub-38 dB acoustics for deployment directly in the working environment. Up to 10 years of long-term support matching industrial equipment lifecycles. Edge AI SDK and pre-trained models to shorten time to first inference. WEDA fleet management for centralized data, model and workload control across distributed nodes. Industrial I/O and triple display with serial, GPIO, dual Ethernet and M.2 NVMe plus dual SATA storage.

Applications

Semiconductor wafer and package automated optical inspection. Medical imaging workstations and clinical review. On-premises edge LLM, VLM and RAG inference where data cannot leave the site. Multi-channel video analytics and smart surveillance. Industrial robotics and machine vision cells needing a local GPU. Containerized and virtualized edge workloads consolidated onto one quiet node.

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