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No Idle GPUs, No Data Leakage: QumulusAI Maximizes GPU Utilization for Multiple Customers on Shared Infrastructure

QumulusAI addresses the challenge of maximizing GPU utilization across multiple customers on shared infrastructure while ensuring strict data isolation. The article explores how multi-tenant GPU environments can eliminate idle compute without compromising security. It highlights the architectural and operational approaches QumulusAI uses to balance efficiency and data privacy at scale.

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By Qumulusai · Ai DeploymentsAmberdData IsolationGpu Cycles
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Key takeaways

01

Multi-tenant GPU infrastructure is becoming essential as AI deployments scale across customers.

02

Organizations must maximize GPU utilization while maintaining strict data isolation.

03

Idle compute reduces efficiency, yet shared environments can introduce security risks if not designed properly.

Multi-tenant GPU infrastructure is becoming essential as AI deployments scale across customers. Organizations must maximize GPU utilization while maintaining strict data isolation. Idle compute reduces efficiency, yet shared environments can introduce security risks if not designed properly.

Optimizing GPU cycles across multiple customers is essential to maintaining performance and cost efficiency. Mazda Marvasti, the CEO of Amberd, explains that Amberd deploys several customer applications on shared infrastructure while ensuring complete data separation. Marvasti says working with QumulusAI allowed his team to configure infrastructure that maximizes GPU utilization without compromising security. He adds that managed services oversight ensures applications run efficiently while preventing cross-customer data exposure.

Video TranscriptExpand ↓

We have to be able to optimize the GPU utilization. So we can't have GPUs sitting around doing nothing. So we want to utilize that available GPU cycles for multiple customers with absolutely no data leakage. The flexibility of working with the Cumulus team to get the infrastructure exactly as we need it was very important because one of the things that we do is that we can deploy multiple customers onto the same infrastructure and they will not have access to each other's data. We have to be able to optimize the GPU utilization. Utilisation. So we can't have GPUs sitting around doing nothing. So we want to utilise that available GPU cycles for multiple customers with absolutely no data leakage. So we have a technology that enables us to deploy applications and then our managed services team to manage those applications for the customers while completely utilizing the GPU. Working with the Cumulus team, were able to set up the infrastructure exactly the way we needed in order for that to happen.

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