We solved a classic GPU underutilization problem for a European research lab. Their high-performance hardware was largely sitting idle. The root cause? Their system reserved an entire graphics card for every single task, large or small. Most workloads ended up using only 10-20% of the available power. Multiple teams then competed for these underutilized cards through a slow, manual management process. The result was zero visibility and zero efficiency.

Our approach? We didn't guess. We tested. On their actual, heterogeneous GPU fleet, we implemented and evaluated two strategies: partitioning the resources of a single GPU and intelligent time-scheduling. The data from these real-world tests gave us the correct configuration.

In eight weeks, we delivered. We built them a custom MLOps platform on Kubernetes, utilizing open-source tools like Ansible, Grafana, and Ray. This transformed their static hardware into a dynamic, shared resource pool. Now, tasks run in parallel, utilization is high, and they have complete control. There is no vendor lock-in. They own the platform outright.

The lesson is straightforward: often, you don't need more hardware. You just need to use properly what you already have.