Resume a project with its settings
A known environment reduces the unknowns of a new experiment. Bring the code version, the dependency file, the settings, and a small evaluation set. Test model reloading and the data format first before changing the batch or the optimizer.
Reproducibility is built at the project level. The same seed does not guarantee identical results after a change in PyTorch version, hardware, or algorithm; so keep the conditions and the gaps you observe.
Use the 80 GB for the right stage
Measure memory at the most loaded point of the loop, not just after importing the model. Training retains more state than plain inference. Gradient accumulation can help you work with smaller micro-batches, but it does not eliminate the weights or the optimizer state.
The A100 supports several compute formats, including BF16. Choose the precision based on the operations and the expected quality, then keep the same rules to compare two experiments.
An alternative must address the diagnosis
If 80 GB is enough but you want to study Hopper, compare the H100 SXM with exactly the same protocol. If your allocations are already overflowing, look instead at the H200 with 141 GB. Conversely, a project that stays well under 48 GB may justify a trial on an RTX A6000 before allocating more capacity.
Book and preserve the resume
Three days are suitable for reproducing a targeted result; 7 days leave room for several variants; 30 days let you organize a longer series with resume points. Associate each checkpoint with its stage and test its loading before the deadline.
The rental is prepared by choosing the A100, the batches, the duration, and the environment, then your details. After choosing crypto and transferring, use "I've paid." Keep the order number in the experiment log to link hardware, period, and results.