{"id":115626,"date":"2026-08-27T14:18:37","date_gmt":"2026-08-27T13:18:37","guid":{"rendered":"https:\/\/mcomputers.cz\/2026\/08\/27\/martin-petr-for-lupa-cz-how-to-build-an-effective-ai-infrastructure\/"},"modified":"2026-08-31T07:59:30","modified_gmt":"2026-08-31T06:59:30","slug":"martin-petr-for-lupa-cz-how-to-build-an-effective-ai-infrastructure","status":"publish","type":"post","link":"https:\/\/mcomputers.cz\/en\/2026\/08\/27\/martin-petr-for-lupa-cz-how-to-build-an-effective-ai-infrastructure\/","title":{"rendered":"Martin Petr for Lupa.cz: How to Build an Effective AI Infrastructure"},"content":{"rendered":"

Martin Petr for Lupa.cz: How to Build an Effective AI Infrastructure<\/h2>\n

Lupa.cz<\/a> has published an interview with our colleague Martin Petr<\/strong>, who discusses the current state of the AI hardware market, its availability, and the challenges companies face today when building their own AI infrastructure.<\/p>\n

High demand for GPU accelerators and other components is affecting not only prices but also delivery times. For specific configurations and platforms, such as NVIDIA HGX or DGX systems, delivery times can reach up to 24 weeks in extreme cases. According to Martin, this makes it all the more important to plan infrastructure well in advance and to take into account not only the required performance but also the availability of individual components when designing the system.<\/p>\n<\/div><\/section><\/div>\n