Strategy and Solutions
The client was a global retail enterprise investing heavily in machine learning to power personalization, demand forecasting, and recommendation systems. While the organization had already deployed GPU resources in its on-premises data centers, these resources were often underutilized due to fragmented infrastructure management and inefficient workload scheduling.
To unlock the full potential of its AI investments and support next-generation personalization models, the retailer partnered with Zymr to modernize its GPU infrastructure.
Despite significant GPU investments, the retailer’s AI teams faced long model training cycles and inconsistent resource availability. GPU workloads were manually scheduled across clusters, leading to idle capacity in some environments and contention in others. Training pipelines lacked dynamic scaling, and infrastructure teams struggled to balance cost control with growing demand from data science teams. The organization required a centralized orchestration framework capable of optimizing GPU usage while supporting diverse AI workloads across departments.
Zymr helped the retailer transform underutilized GPU infrastructure into a high-performance AI platform. The modernized environment accelerated model training, improved resource efficiency, and enabled the rapid development of new personalization capabilities.
Zymr implemented a modern AI infrastructure layer designed for efficient GPU orchestration and scalable training environments.
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