Reduce the project without losing its question
For a reproduction, start with a subset of data that preserves the important classes or difficulties. Get loading, training, validation, and saving working on this scope. This intermediate result does not replace the full experiment, but it lets you verify the computation path before committing the entire duration. Decide in advance which signal will justify moving to the full corpus or a larger configuration.
Make the test fit within 24 GB
Separate the levers: weight format, compute precision, example length and microbatch. Change only one lever between two memory measurements. An adjustment of a few parameters may fit this envelope, depending on the model and activations, but full training of the same model can have a very different footprint. Keep the observed peak with the exact parameters, including those used for validation.
Test a resume from a checkpoint and verify a few outputs after reloading. The useful outcome of a first plan is often an experiment you can rerun without rebuilding your memory and data choices.
Compare generations on a finished result
Make sure your CUDA/PyTorch environment and compiled extensions support Ampere. For an old research repository, a locked dependency file makes diagnosis easier. The RTX 4090 lets you compare a more recent architecture with the same 24 GB capacity. The RTX A6000 answers a different question: keeping more memory, especially if shortening examples would distort your protocol.
Plan a progression over 3, 7 or 30 days
Three days are suitable for a reproducible end-to-end test. Seven days allow a few variants on a stabilized corpus. Thirty days can cover more repetitions after the recipe has been validated. Organize your data and exports, select duration and quantity, then enter your first name, last name and email. The rental is paid in crypto without an ID document request; your order keeps the tracking steps.