# On-premise AI is back: what it takes to make it work

DevFeed: [On-premise AI is back: what it takes to make it work](<https://devfeed.tech/articles/on-premise-ai-is-back-what-it-takes-to-make-it-work-79780.md>)

Original publisher: [Read original article](<https://www.spectrocloud.com/blog/on-premise-ai-is-back-the-server-is-the-easy-part>)

Author: Ant Newman

Published: 2026-09-10T08:00:00Z

Content type: article

Language: en

Sources: [Spectro Cloud Feed](<https://devfeed.tech/sources/spectro-cloud-feed.md>)

Topics: [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [Local AI](<https://devfeed.tech/topics/local-ai.md>), [scheduling](<https://devfeed.tech/topics/scheduling.md>)

Tags: [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [on-premise](<https://devfeed.tech/tags/on-premise.md>)

## AI overview

The article explains how enterprises can evaluate and operate on-premises AI inference alongside cloud and frontier models. It covers workload benchmarking, GPU memory sizing, full ownership costs, governance and edge deployment. It argues that reliable shared services require scheduling, tenant isolation, model routing, observability and coordinated stack updates. Its cost savings examples are vendor-modeled scenarios whose results depend on utilization, staffing, facilities and commercial terms.

## Source excerpt

On-premise AI is back. The four reasons enterprises are bringing inference in-house, the AI server you actually need, and how to run it as a reliable shared service.