The demand for dedicated computational resources for AI tasks is surging. However, the substantial investment required for high-performance GPUs, coupled with in-creasing market scarcity and extended lead times, poses significant challenges. Simul-taneously, underutilization of these resources is a frequent issue. This paper proposes a solution leveraging cloud-native approaches to utilize unused resources available elsewhere, imposing minimal overhead on the originating cluster. We will present how an “origin” cluster can seamlessly peer with a “donating” cluster, offloading tasks to remote hardware with complete transparency and superior efficiency com-pared to current technologies.
GPU Offloading in MLOps: Navigating the Multicloud Ecosystem for Flexible AI/ML Deployments / Risso, Fulvio; Zangari, Giuseppe. - ELETTRONICO. - (2024), pp. 76-80. (Intervento presentato al convegno Conferenza GARR 2024 - Navigare la complessità. Infrastrutture e competenze digitali per la ricerca tenutosi a Brescia (IT) nel 29-31 maggio 2024) [10.26314/GARR-Conf24-proceedings-13].
GPU Offloading in MLOps: Navigating the Multicloud Ecosystem for Flexible AI/ML Deployments
Fulvio Risso;Giuseppe Zangari
2024
Abstract
The demand for dedicated computational resources for AI tasks is surging. However, the substantial investment required for high-performance GPUs, coupled with in-creasing market scarcity and extended lead times, poses significant challenges. Simul-taneously, underutilization of these resources is a frequent issue. This paper proposes a solution leveraging cloud-native approaches to utilize unused resources available elsewhere, imposing minimal overhead on the originating cluster. We will present how an “origin” cluster can seamlessly peer with a “donating” cluster, offloading tasks to remote hardware with complete transparency and superior efficiency com-pared to current technologies.| File | Dimensione | Formato | |
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| 24GARR - GPU Offloading.pdf accesso aperto 
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https://hdl.handle.net/11583/3002353
			
		
	
	
	
			      	