Get the AI system your workload actually needs.

Not a parts list: a model answering locally, a notebook session and a training run, recorded on our own machines.

Memory decides how much context a card can hold on a large model. Multi-card RTX PRO 6000 towers use the 300 W Max-Q edition, which keeps 93% or more of the generation speed at half the power.

Both are two-card Threadripper towers. The difference is what one card holds.

Memory from this page’s rule: 4-bit weights plus 20% for cache and overhead, at a short context and batch 1. The speed figures above are single-card runs; we have not found a published two-card run for these exact cards.

If your models fit in 32 GB a card, the 5090 pair is the sensible buy. If one model has to sit on one card, or an LLM and an image pipeline run at once, the PRO pair holds it.

Buying one card now and a second next year? Say so. We explain the expansion options in the platform you choose, and the quote says what adding the second card will need.

DIY fits when you have the hours to integrate and support it. Cloud fits when usage is bursty or needs cluster scale on demand. An OEM tower fits when IT mandates a fleet vendor. Here is how the four compare for the rest.