Case Study: European Research Laboratory maximizes GPU utilization with Evrone’s open-source MLOps platform

European Research Laboratory unlocks GPU utilization in 2 months with Evrone

Evrone was approached by a European research laboratory facing significant underutilization of its high-performance GPU infrastructure. Despite running advanced machine learning, AI, and data analysis workloads, the lab's system reserved an entire GPU per task, leaving most of each card's capacity idle. Manual resource allocation and a lack of centralized scheduling created inefficiencies and a lack of visibility for multiple teams.

Evrone solved this by building a secure, open-source MLOps platform on Kubernetes. The solution enabled the safe sharing of GPUs across users and tasks, allowing multiple jobs to run in parallel. This significantly improved overall GPU utilization, eliminating idle time and providing managers with full visibility into resource allocation. The entire project was delivered on time in just two months, allowing the lab's engineers to run workloads without waiting for resources to free up.