Sizing beyond the weights file
For a generation service, the model size on disk does not describe all the memory required. Add the working buffers and the cache used during generation. Build a set of requests that includes your long inputs and several levels of concurrency, then measure the resulting peak.
Use this margin to choose an explicit context limit and queue. A single successful request is not enough to set the capacity of a service used by multiple clients.
Keeping a traceable CUDA path
Lock Python, PyTorch, the inference engine, and its extensions together. If you change the precision or the attention engine, rerun the same examples and also compare output quality. The H200 brings memory capacity; it does not choose acceptable generation parameters for you.
Archive the tokenizer, the model revision, and the service configuration alongside your results. You can then distinguish a software change from a hardware change.
The right pattern for scaling up in memory
If your workload already fits within 80 GB with sufficient margin, compare the H100 PCIe plan before choosing the H200. If a single process needs more than 141 GB, look into the MI300X and ROCm compatibility, or prepare a multi-card split on B200. Adding batches does not automatically enlarge the memory of a single process.
From load testing to the plan
Over 3 days, focus on a limited context/concurrency matrix. One week lets you add errors, restarts, and exports; 30 days suit an already organized iteration campaign. Set aside time to retrieve the results and the final manifest.
The configuration asks for the number of batches, the setup, and your contact details. Crypto payment is offered without KYC or an identity document. After the transfer, report it with “I've paid,” then check the payment status in your order.