Automated Dynamic AI Inference Scaling on HPC-Infrastructure: Integrating Kubernetes, Slurm and vLLM

Tim Trappen; Robert Keßler; Roland Pabel; Viktor Achter; Stefan Wesner

doi:10.1145/3774902.3776632

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arXiv · 2511.21413

Automated Dynamic AI Inference Scaling on HPC-Infrastructure: Integrating Kubernetes, Slurm and vLLM

Tim Trappen·Robert Keßler·Roland Pabel·Viktor Achter·Stefan Wesner

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Abstract

Due to rising demands for Artificial Inteligence (AI) inference, especially in higher education, novel solutions utilising existing infrastructure are emerging. The utilisation of High-Performance Computing (HPC) has become a prevalent approach for the implementation of such solutions. However, the classical operating model of HPC does not adapt well to the requirements of synchronous, user-facing dynamic AI application workloads. In this paper, we propose our solution that serves LLMs by integrating vLLM, Slurm and Kubernetes on the supercomputer \textit{RAMSES}. The initial benchmark indicates that the proposed architecture scales efficiently for 100, 500 and 1000 concurrent requests, incurring only an overhead of approximately 500 ms in terms of end-to-end latency.

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BibTeX RIS

Tim Trappen, Robert Keßler, Roland Pabel, Viktor Achter, Stefan Wesner. 2025-11-26. Automated Dynamic AI Inference Scaling on HPC-Infrastructure: Integrating Kubernetes, Slurm and vLLM. https://doi.org/10.1145/3774902.3776632

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