Sweat dedicated GPU clusters to beat cloud on cost

Over the past year, demand for GPUs to train generative AI models has soared. Some organizations have invested in GPU clusters costing millions of dollars for this purpose. Cloud services offered by the major hyperscalers and a new wave of GPU-focused cloud providers deliver an alternative to dedicated infrastructure for those unwilling, or unable, to purchase their own GPU clusters.

There are many factors affecting the choice between dedicated and cloud-based GPUs. Among these are the ability to power and cool high density GPU clusters in a colocation facility or enterprise, the availability of relevant skills, and data sovereignty. But often, it is an economic decision. Dedicated infrastructure requires significant capital expenditure that not all companies can raise. Furthermore, many organizations are only just beginning to understand how (and if) they can use AI to create value. An investment in dedicated equipment for AI is a considerable risk, considering its uncertain future.