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title: Architecting GPUaaS for Enterprise AI On-Prem | daily.dev
description: A detailed walkthrough of building a self-service GPU-as-a-Service (GPUaaS) platform on a single Cisco UCS C845A server running Single Node OpenShift. The...
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# Architecting GPUaaS for Enterprise AI On-Prem

**[Towards Data Science](https://daily.dev/sources/tds)** · 16 min read · 3 upvotes · 0 comments

## Summary

A detailed walkthrough of building a self-service GPU-as-a-Service (GPUaaS) platform on a single Cisco UCS C845A server running Single Node OpenShift. The architecture covers three planes: a scheduling plane using a calendar-based reservation system, a control plane built around a Kubernetes-style Python reconciler loop, and a runtime plane providing isolated per-tenant namespaces with full ML tooling. GPU resource management uses NVIDIA MIG partitioning (hardware-level isolation) combined with time-slicing for multi-tenant sharing. The post also introduces a 'tokenomics' cost framework that reduces infrastructure spend to cost-per-million-tokens, enabling on-prem vs. cloud comparisons and business case justification for enterprise AI infrastructure.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/architecting-gpuaas-for-enterprise-ai-on-prem/>

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---

Tags: [#kubernetes](https://daily.dev/tags/kubernetes), [#gpu](https://daily.dev/tags/gpu), [#openshift](https://daily.dev/tags/openshift), [#mlops](https://daily.dev/tags/mlops), [#multi-tenancy](https://daily.dev/tags/multi-tenancy)

[View this post on daily.dev](https://daily.dev/posts/architecting-gpuaas-for-enterprise-ai-on-prem-u2jlj0atd)

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