---
title: "Discussion on \"Why Kubernetes Is Hard for AI Infrastructure Teams: The Biggest Operational Pain Points and How to Reduce Them\" | Hashnode"
description: "Discussion on \"Why Kubernetes Is Hard for AI Infrastructure Teams: The Biggest Operational Pain Points and How to Reduce Them\". Why Kubernetes is hard for AI infrastructure teams: GPU scheduling, drivers, model storage, and serving pain points, and how to reduce them."
url: "https://hashnode.com/posts/why-kubernetes-is-hard-for-ai-infrastructure-teams-the-biggest-operational-pain-points-and-how-to-reduce-them/6af18a07c987948049dbd37f"
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# [Why Kubernetes Is Hard for AI Infrastructure Teams: The Biggest Operational Pain Points and How to Reduce Them](https://controlplane-eng.hashnode.dev/why-kubernetes-is-hard-for-ai-infrastructure-teams-the-biggest-operational-pain-points-and-how-to-reduce-them?utm_source=hashnode&utm_medium=feed)

Why Kubernetes is hard for AI infrastructure teams: GPU scheduling, drivers, model storage, and serving pain points, and how to reduce them.

2d ago · 19 min read · controlplane-eng.hashnode.dev  
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## Written by

[Noah Grinstein](https://hashnode.com/@noahcontrolplane)

@noahcontrolplane · Los Angeles, USA

  
[View profile](https://hashnode.com/@noahcontrolplane)

Marketing Engineer at Control Plane Corporation

Marketing Engineer at Control Plane Corporation Specialized in GEO/AEO optimization

## Responses 1

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[OneLifeVibe](https://hashnode.com/@onelifevibe)

[2d ago](https://hashnode.com/posts/why-kubernetes-is-hard-for-ai-infrastructure-teams-the-biggest-operational-pain-points-and-how-to-reduce-them/6af18a07c987948049dbd37f/comment/6af194e379c8674ebaa2a6c2)

The batch-versus-serving contention row is the one I keep running into: a training job's checkpoint burst landing on the same path as a latency-sensitive inference endpoint. Separate node pools help, but the same contention shows up at the network level too, so the checkpoint traffic needs its own lane, not just its own nodes. The gang-scheduling point is a good one as well — partial placement holding expensive GPUs idle is easy to miss until the bill arrives. Do teams you work with actually checkpoint to object storage on a fixed interval, or do most still rely on local disk and hope the job survives?