Running Large-Scale GPU Workloads on Kubernetes with Slurm
Slinky, developed by SchedMD and now part of NVIDIA, facilitates the integration of Slurm cluster management with Kubernetes. This integration allows for efficient management of large-scale GPU workloads, supporting advanced NVIDIA architectures. Production deployments have shown that Slinky can scale to over 8,000 GPUs while maintaining performance parity with traditional Slurm clusters.
- ▪Slinky enables native Slurm cluster management on Kubernetes using Custom Resource Definitions.
- ▪It supports automated GPU management and topology-aware scheduling for advanced NVIDIA architectures.
- ▪Production deployments at NVIDIA have demonstrated Slinky's ability to scale to over 8,000 GPUs.
2 outlets in our directory ran this story, first to last over 12 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | NVIDIA Technical Blog |
| Canonical URL | https://developer.nvidia.com/blog/running-large-scale-gpu-workloads-on-kubernetes-with-slurm/ |
| Publication time | Sun, 24 May 2026 14:25:36 +0000 |
| Retrieval time | 2026-05-24T14:37:33.279Z |
| Last seen | 2026-05-24T14:37:33.279Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 7xAN8mNG-8Av · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Data Center / Cloud English中文 Running Large-Scale GPU Workloads on Kubernetes with Slurm Apr 09, 2026 By Anton Polyakov, Fagani Hajizada, Marlow Warnicke and Skyler Malinowski Like Discuss (0) L T F R E AI-Generated Summary Like Dislike Slinky, developed by SchedMD (now part of NVIDIA), enables native Slurm cluster management on Kubernetes by representing all Slurm daemons as Kubernetes Custom Resource Definitions, supporting full Slurm lifecycle orchestration and high availability without relying on Slurm's native HA.Integration with the NVIDIA GPU Operator and DRA/ComputeDomains allows automated GPU management, topology-aware multinode scheduling, and per-job GPU monitoring, supporting advanced NVIDIA architectures like GB200 NVL72 with dynamic Internode Memory Exchange and topology…
Excerpt limited to ~120 words for fair-use compliance. The full article is at NVIDIA Technical Blog.
Discussion
0 commentsMore from NVIDIA Technical Blog
