# When Kubeflow Meets Cilium: Debugging 60% Idle GPUs in Kubernetes

> **Platform:** [SPIDITS AI](https://spidits.com/) — Real-Time AI News & Market Intelligence  
> **Published:** 2026-07-23T11:33:00.000Z  
> **Category:** PRODUCT_LAUNCH  
> **Impact Score:** 80/100  
> **Primary Source:** [CNCF Blog](https://www.cncf.io/blog/2026/07/23/when-kubeflow-meets-cilium-debugging-60-idle-gpus-in-kubernetes)  
> **Canonical Citation:** [https://spidits.com/timeline/when-kubeflow-meets-cilium-debugging-60-idle-gpus-in-kubernetes](https://spidits.com/timeline/when-kubeflow-meets-cilium-debugging-60-idle-gpus-in-kubernetes)

## Executive Summary
The symptom that made no sense The first time we saw it, we didn't trust the dashboard.

## Why It Matters (Strategic Analysis)
Crucially, this shifts the focus from individual system correctness to the collective impact of topology-agnostic scheduling and topology-aware network policies, highlighting the need for integrated solutions in Kubernetes.

## Referenced Coverage & Sources
- **[CNCF Blog](https://www.cncf.io/blog/2026/07/23/when-kubeflow-meets-cilium-debugging-60-idle-gpus-in-kubernetes)**: When Kubeflow meets Cilium: Debugging 60% idle GPUs in Kubernetes — _The symptom that made no sense The first time we saw it, we didn't trust the dashboard. A distributed training job was scheduled and healthy - every pod was running, no crashes, no OOMKills, nothing in..._

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