Collection: Edge AI Nodes

Intelligence Doesn't Always Live in the Cloud

There's a moment in every AI deployment when the team realizes: we can't keep sending everything to the cloud. Latency is too high. Bandwidth costs are unsustainable. Data sovereignty requirements are tightening. The model needs to run here — in the facility, at the edge of the network, close to where the data is generated and the decision needs to be made.

Edge AI Nodes are the answer to that moment. Compact, ruggedized, power-efficient compute platforms designed to run AI inference workloads in environments where a full data center rack isn't an option — but performance still matters. DVUN's edge AI node lineup covers the full range of edge deployment scenarios: from small form factor inference appliances for office or lab environments, to hardened industrial nodes for factory floors and remote facilities.

Six Reasons to Deploy at the Edge with DVUN

• Sub-10ms Inference Latency: When your application can't afford a round-trip to the cloud, local inference changes everything. Edge nodes deliver real-time response where it counts.
• Compact Form Factors: Mini-ITX to short-depth 1U rack configurations that fit in network closets, equipment rooms, and non-standard spaces.
• Low Power Envelopes: Designed for environments without dedicated power infrastructure — most nodes operate within standard 15A circuit limits.
• Industrial Temperature Range: Select models rated for 0–50°C or wider operating ranges for non-climate-controlled environments.
• Framework-Ready: Pre-validated with ONNX Runtime, TensorRT, OpenVINO, and other edge inference frameworks out of the box.
• Secure Local Processing: Keep sensitive data on-premise. No data leaves the facility — critical for healthcare, finance, and defense-adjacent applications.

Two Scenarios Where Edge Nodes Change the Game

The Manufacturing Quality Control Line: A robotics team needs to run real-time visual inspection inference on a production line. Cloud latency is 80ms — too slow for the conveyor speed. A DVUN edge AI node mounted in the line's control cabinet runs the vision model locally at under 8ms, with no internet dependency and no data leaving the facility. Explore our Accelerator Cards for add-in GPU options to boost edge inference throughput.

The Distributed Inference Network: A private AI cloud builder needs to deploy inference capacity across 12 regional locations without building full data center infrastructure at each site. DVUN edge nodes provide a standardized, remotely manageable inference platform that can be shipped, racked, and activated at each location within days. See our Ready Systems for pre-configured multi-site deployment kits.

Technical Specifications Overview

• Form factors: Mini-ITX, NUC-style, short-depth 1U rack, DIN-rail mount
• Accelerator options: integrated GPU, PCIe add-in card slots, NPU modules
• Memory: 16GB to 128GB DDR5 configurations
• Storage: NVMe M.2 and 2.5" SATA options, up to 8TB local storage
• Connectivity: 2.5GbE to 10GbE, Wi-Fi 6E on select models, optional 5G
• Power: 65W to 350W TDP range depending on configuration
• Management: Remote KVM, IPMI, and cloud-managed options available

Deploy Closer. Respond Faster.

The edge is where AI becomes real — where models stop being experiments and start being infrastructure. DVUN's edge AI node collection is built for teams ready to make that transition. Request a quote for multi-site deployments or bulk orders, and let our team help you design an edge architecture that scales.