Growing the data before rewriting the software
A segmentation on larger images, a series of intermediate tensors or several components loaded together can justify 48 GB. Start from an example that already works, then progressively increase the dimension that was limiting you. This approach gives a more precise answer than changing hardware, model and library all at once.
Do not confuse the card's capacity with the amount available for your tensors. The GPU context, libraries and buffers also use memory; allow for a margin in the scenario you choose.
Preserving the Ampere compatibility chain
Document the project-specific CUDA extensions and how they were installed. Test their execution with your usual data types and dimensions. Compatibility with Ampere does not mean that all versions of all packages combine without work.
If you rent several lots, choose between distributing independent tasks or splitting a computation. In both cases, assign the processes and outputs explicitly; the program must know how to use the additional cards.
48 GB: comparing capacity and goal
The RTX 6000 Ada keeps 48 GB and lets you study a different generation with the same memory budget. The A40 is another 48 GB Ampere option to compare for batch processing. If the model still exceeds this capacity, the 80 GB A100 SXM can avoid prematurely splitting the program.
A rental designed to be resumed
Three days let you validate the scaling up of the scenario. One week can cover processing a corpus with output checks. Over 30 days, prepare a manifest of completed files and a resumption that does not redo what has already succeeded.
Record the duration and the lots in the configuration, choose your setup then enter first name, last name and email. Crypto payment follows its network and the amount shown. After your transfer, the "I have paid" report remains available in the order, along with the verification status.