International Journal on Science and Technology

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Virtualized GPU for AI in Enterprise Storage: Cost-Optimized Solutions

Author(s) Venkatachala Nivas Chainuru
Country United States
Abstract The integration of artificial intelligence capabilities into enterprise operations has become a strategic imperative across industries, yet organizations face significant challenges related to infrastructure costs, operational complexity, and deployment efficiency. This article examines how Dell Technologies, VMware, and NVIDIA have collaborated to develop virtualized GPU solutions specifically designed for enterprise storage environments. The Dell Validated Design for Virtualizing GPUs for AI with VMware and NVIDIA enables organizations to leverage virtualized GPU capabilities within existing VMware infrastructure, creating a more flexible and cost-effective approach to AI implementation. The solution incorporates NVIDIA AI Enterprise, a comprehensive software suite optimized for virtualized environments, allowing organizations to virtualize and containerize AI workloads on NVIDIA-Certified Systems. Performance analyses demonstrate that virtualized environments can achieve performance comparable to bare-metal implementations while delivering substantial operational benefits including reduced deployment time, improved operational efficiency, enhanced resource utilization, and lower total cost of ownership. The unified infrastructure approach supports diverse AI requirements across enterprise departments including human resources, information technology, and customer service, enabling organization-wide AI integration while maintaining operational continuity.
Keywords Keywords: Virtualized GPU, Enterprise AI, NVIDIA AI Enterprise, VMware vSphere, Infrastructure Optimization
Field Computer
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-11
Cite This Virtualized GPU for AI in Enterprise Storage: Cost-Optimized Solutions - Venkatachala Nivas Chainuru - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2341
DOI https://doi.org/10.71097/IJSAT.v16.i1.2341
Short DOI https://doi.org/g87rhk
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