A readable single-card application
Start with a process that loads its model, processes a batch, and exports a result. Set the input size and the number of requests. This simple scenario makes it possible to identify whether the next effort should target GPU memory, data, or Python logic.
With 80 GB, you can explore larger workloads than with a 24 or 48 GB card, while keeping a single memory domain to manage. Still, allow a margin for your engine's temporary allocations.
Observe transfers as much as the kernel
A program that reloads the same data between two calls can spend a significant portion of its time outside useful compute. Time CPU preparation, transfer, execution, and result retrieval separately. For CUDA measurements, account for asynchronous execution so you don't only measure job submission.
Validate that it works with your PyTorch version and the project's compiled libraries. Keep a run without optimization as a baseline before modifying the execution path.
Compare without confusing the H100 formats
H100 SXM and H100 PCIe do not describe the same hardware configuration. Choose SXM if your study justifies measuring its behavior for distributed exchanges. Choose H200 instead when your single-card workload exceeds 80 GB. A100 SXM remains an alternative to consider for an already-qualified Ampere environment.
Rent to produce a decision
A 3-day plan can be used to establish the initial profile. Over 7 days, test several batch sizes and keep a comparison report; over 30 days, organize runs and their tracking with stable identifiers.
Choose the duration, the number of batches, and the setup in the configurator, then create your account or log in. Payment follows the selected asset and network. After your transfer, "I've paid" adds a report to the order, which you can find in your Kernodeck account.