GPU Memory Sizing Guide: H100 vs H200 for 70B Inference and KV Cache

A model that fits on paper can still fail when real users arrive. The missing line is often not the model weights: it is the key-value cache, runtime workspace, or a parallel layout that cannot distribute memory as evenly as the spreadsheet assumes.

This GPU memory sizing guide answers a practical procurement question: how many H100 or H200 GPUs should you budget for a 70-billion-parameter inference service? It includes a reusable calculation, a concurrency table, two original diagrams, and an acceptance checklist. All designs are hypothetical; the arithmetic was executed in Python, but no GPU benchmarks or deployment tests were run for this article.

X Follow @ipexptobe

AI Checkpoint Storage Sizing Guide: Bandwidth, Capacity and a 256-GPU Design Example

A GPU cluster can have a healthy training fabric and still miss its checkpoint window. The useful buying question is not “How fast is this storage appliance?” It is “Can this complete training state reach recoverable storage before the deadline, while the other jobs keep running?” This guide provides a reusable sizing worksheet, a worked 256-GPU example, a bottleneck matrix and a deployment acceptance checklist.

Scope: Everything in the worked design is hypothetical. Bandwidth values are planning assumptions, not benchmark results or vendor performance promises. Calculations use decimal GB/TB and single-direction network rates. No GPU, storage or failure-injection tests were executed for this article; the sizing arithmetic was executed locally.

X Follow @ipexptobe