Make the scenario fit before expanding
Choose a short objective: process a set of documents, adapt a few parameters or generate a series of images. Load a complete example and note the memory peak. If the scenario fails, reduce one dimension at a time — batch, length or resolution — to understand which one governs the requirement.
Gradient accumulation can help build an effective batch from micro-batches. It does not remove the need to measure the training step, whose states take up more space than a simple inference pass.
Leaving the trial notebook behind
Once the prototype works, export its steps into a script that takes an explicit configuration. Fix the model, the inputs, the package versions and the output location. A notebook run in an unknown order becomes hard to compare after several days of changes.
Keep a small test that checks CUDA loading and produces a known output. It will quickly tell you if an update to PyTorch or an extension has changed the project path.
When to go beyond 24 GB
Move to the RTX 5090 if 32 GB and a compatible Blackwell stack meet your needs. Choose a 48 GB card instead, such as the RTX A6000, if allocations clearly exceed 24 GB. For an application already qualified on Ampere with modest needs, the RTX 3090 is also a relevant point of comparison.
Giving each duration a clear output
In 3 days, aim for a re-runnable script and an initial measurement. In 7 days, compare parameters without changing the dataset at the same time. Over 30 days, plan several trials with checkpoints and a summary kept outside the workspace.
To rent, choose the lots, the duration and the environment, then create your account or log in. You then select the asset and the crypto network. Once the transfer is made, use "I have paid" and find the status of the order in your account.