Prepare a complete software migration
List the components that execute GPU compute: PyTorch, attention libraries, quantization, and any custom operators. A portable Python script may depend on an exclusively CUDA extension. Verify each component against the chosen ROCm version before launching a major campaign.
In PyTorch for ROCm, the torch.cuda interfaces are still used to refer to the GPU. So don't mechanically replace every occurrence with "hip": identify the installed backend and test a small real computation.
Leverage 192 GB with a representative test
This capacity makes room for large models, activations, or caches. It does not by itself let you infer the maximum size of a model: precision, input length, adaptation method, and temporary spaces all change the total requirement.
Prepare a case that includes your longest inputs and a full checkpoint. Compare the numerical results of the migration with a tolerance defined for your task, rather than an implicit requirement for identical files across platforms.
When to keep an NVIDIA path
If a core extension has no usable ROCm path, the H200 SXM with 141 GB can simplify the project by staying on CUDA. For software that is already portable but a smaller workload, also compare the real requirement against an 80 GB card. The additional memory must solve an identified problem, not just replace a diagnosis.
Split the rental into validation stages
Use 3 days to install, test, and record the gaps; 7 days to stabilize dependencies and compare your outputs; 30 days for a campaign whose ROCm path is validated. Record the system distribution and library versions in the manifest.
Select MI300X, the duration, the batches, and a setup suited to your ROCm stack. First name, last name, and email remain the order details; no KYC identity documents are requested. Then choose the crypto method and report the transfer with "I've paid."