Distinguish the loaded model from the executed request
A model that loads correctly does not yet validate your use case. The KV cache used during generation can grow with the sequences retained; simultaneous requests also increase the footprint to observe. Prepare three groups of texts: short, intermediate and close to your maximum length. For each, set the number of output tokens so you compare equivalent tasks.
Measure memory at the hard point
Record loading, prompt processing and generation separately. A sample of average conversations can mask the case that saturates the card. Look for the peak on long inputs with the concurrency you actually target, then keep a margin for your inference engine's buffers. If you test a quantized or offloaded cache, also note its effect on response time and on your quality metric.
The 141 GB also serve to examine a larger batch in training. In that case, gradients, activations and optimizer states must be included in the budget, not just the weights.
Keep a readable comparison with the H100
The H200 belongs to the Hopper family. Check the CUDA versions and attention kernels supported by your environment, then lock those versions during the trial. If all your representative workloads already fit within 80 GB, compare the H200 against the H100 SXM with the same model and the same precision. Additional memory is useful when it enables an identified goal, such as more context or less fragmentation of the work.
From capacity test to full campaign
Book three days to map memory, context and concurrency; seven days to compare several caching strategies; thirty days for repeated evaluations on an evolving corpus. Prepare the data, generation parameters and exports before ordering. The form lets you choose the duration and number of cards, then pay in crypto without KYC. First name, last name and email ensure follow-up; no identity document is requested.