GPU inefficiency is rarely a hardware problem. Evrone encountered this pattern while working with a European research lab.
The real bottlenecks
◻️ One GPU per job
◻️ Low average utilization
◻️ No centralized scheduling
Evrone’s technical focus
◻️ Hardware-aware testing
◻️ Open-source first
◻️ Automation everywhere
Stack overview ⚙️
◻️ Kubernetes
◻️ Ray.io
◻️ Prometheus + Grafana
◻️ Keycloak
◻️ FluxCD
What changed
① GPUs became shared assets
② ML workloads scaled horizontally
③ Engineers gained autonomy
This case reinforced Evrone’s belief: Good MLOps is about systems, not tools. Evrone treated observability as a first-class concern, not an optional feature. Detailed GPU metrics allowed teams to understand how workloads behaved under real conditions. This visibility made optimization continuous rather than reactive. Over time, the platform became a learning system for both engineers and researchers.
- The entire setup follows open-source best practices, allowing full customization by engineers.
- Real-time GPU metrics provide continuous insight into workloads and utilization.
- Security is baked into the platform, with Keycloak handling authentication for all services.
- FluxCD manages deployments, enabling reproducible and automated infrastructure updates.
- The result: ML experiments run faster, scale seamlessly, and use GPU resources optimally.
How Evrone Helped a Research Lab Fix GPU Underutilization.
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