Your AI, Your Way

Most enterprises invest in high-end compute and connect it to network infrastructure that was designed for office traffic. Email, file shares, video calls. The assumption is that if the GPUs are powerful enough, the rest of the stack will keep up. It does not. A GPU can only process data as fast as the network delivers it. When the network runs at 200 gigabit and the workload demands 800, compute...

Your first AI proof of concept succeeded. Your platform did not. The technology worked. The use case delivered results. Then someone asked how to scale it to more users, more use cases, and a platform that IT can actually manage. That question landed on desks that were never involved in the original project. Most organizations do not start from a blank canvas. They already run systems, enforce pol...

No power, no compute. That is the reality in the Netherlands today. When Nvidia assessed Europe, the message was clear: there is no sovereign AI infrastructure here. Scandinavian countries have electricity. The Netherlands does not. If nothing changes, the next generation of AI talent will have to leave the country to do serious work. Lorentz is the response. A regional AI initiative built by entr...

You cannot secure what you cannot see. When cloud adoption started, employees picked their own tools without IT knowing. They called it Shadow IT. The same pattern is now repeating with AI. Developers pull models from Hugging Face because it is convenient. Over one hundred thousand models live there. Most have never been security checked. A well-known vendor recently published a PyTorch container...

Giving people AI tools is not the same as AI adoption. Most employees are driven by their inbox. Add a strategic AI project on top, and enthusiasm alone will not create capacity. Without structure, AI becomes a side project for one eager person while leadership has no visibility into the risks underneath. At TU Eindhoven, the Supercomputing Center grew its AI team from one engineer to five in eigh...

Most AI pilots never reach production. The technology works. The use case makes sense. Then the cloud bill arrives. Costs spiral before anyone sees it coming. It starts small. A few GPUs in the cloud. Reasonable invoices. Then the project scales. Storage costs appear. Data transfer fees stack up. That monthly cloud bill? It can multiply by thirty before finance even flags it. Meanwhile, GPUs sit i...

Many organizations wait for the right hardware, the right budget, or the right moment to begin investing in artificial intelligence. Trivium Packaging did not. They launched their first AI chatbot on a single server, without a GPU. Each response took approximately ten minutes. The system was slow, inelegant, and limited—but it functioned. Ten months later, Trivium operates a full Kubernetes cluste...

Initiating AI workloads in the cloud is straightforward. GPUs can be provisioned quickly, experiments launched immediately, and early results demonstrated to leadership—without capital expenditure or procurement delays. The challenge emerges at scale. As systems move into production, costs escalate. Finance questions why cloud spend doubled last quarter. Security teams seek clarity on where sensit...