# Artificial Intelligence, Data Center, Edge Computing, Networking

Published articles for Artificial Intelligence, Data Center, Edge Computing, Networking.

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## AI inferencing is headed for the network edge

DevFeed: [AI inferencing is headed for the network edge](<https://devfeed.tech/articles/ai-inferencing-is-headed-for-the-network-edge-50524.md>)

Original publisher: [Read original article](<https://www.networkworld.com/article/4221248/ai-inferencing-is-headed-for-the-network-edge.html>)

Author: Neal Weinberg

Published: 2026-09-14T10:00:00Z

Content type: article

Language: en

Sources: [Network World](<https://devfeed.tech/sources/network-world.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Network](<https://devfeed.tech/topics/network.md>), [Internet of things](<https://devfeed.tech/topics/iot.md>), [data gravity](<https://devfeed.tech/topics/data-gravity.md>), [Critical Infrastructure](<https://devfeed.tech/topics/critical-infrastructure.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [data centers](<https://devfeed.tech/topics/data-centers.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [artificial-intelligence-data-center-edge-computing-networking](<https://devfeed.tech/tags/artificial-intelligence-data-center-edge-computing-networking.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [critical-infrastructure](<https://devfeed.tech/tags/critical-infrastructure.md>), [data](<https://devfeed.tech/tags/data.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [data-gravity](<https://devfeed.tech/tags/data-gravity.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [genai](<https://devfeed.tech/tags/genai.md>), [inferencing](<https://devfeed.tech/tags/inferencing.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [iot](<https://devfeed.tech/tags/iot.md>), [network](<https://devfeed.tech/tags/network.md>), [networking](<https://devfeed.tech/tags/networking.md>), [real-time](<https://devfeed.tech/tags/real-time.md>)

### AI overview

The article examines the shift toward running AI inference at the network edge, where IoT and other sensor data can be processed locally for real-time responses. It describes enterprise drivers including data volume, processing cost and delay, and data residency, privacy, and sovereignty concerns.

### Source excerpt

Processing IoT/OT data as close as possible to the source has been a longstanding goal for IT leaders. Recent technological advances are now making it possible to perform full-blown AI inferencing at the network edge, opening the door for game-changing applications that can respond autonomously to sensor data in real time. "The combined rapid growth of edge data and the imperative that businesses now have to leverage AI capabilities for business value are leading inevitably toward significant edge AI growth," says Gartner analyst Thomas Bittman. By 2028, more than two-thirds of enterprise-managed data will be created and processed outside the data center or cloud, Gartner predicts, and more than two-thirds of all enterprises globally will deploy edge AI by 2029, up from 10% in 2025. Likewise, IDC expects that half of all enterprise AI inference workloads will run on endpoints or edge nodes by 2030, according to the firm's 2026 FutureScape IT predictions. "Enterprises are increasing edge IT investments to support genAI/AI inference, with strong momentum in healthcare, finance, and manufacturing," says Olga Yashkova, IDC's research manager for edge AI strategies. What's driving edge AI? A variety of factors are coming together to make edge AI a high priority for IT executives. Data gravity: In 2025, there were about 11.7 billion IoT devices installed, and that number is growing by 9% a year, says Gartner. The volume of data generated by always-on devices like security cameras, traffic cams, or sensors embedded in critical infrastructure, is staggering. Bittman points out that as much as 90% of edge data goes unprocessed. "The combination of improving technologies available for the edge and the importance of leveraging that data (especially with AI) will significantly increase the percentage processed over time," he says. "The volume of data and the cost and delay of processing that data elsewhere will put more pressure on finding solutions to filter, process, and even