The H100 is not just a faster GPU. It is a tightly balanced inference machine where tensor throughput, memory movement, cache behavior, and interconnect design have to work together under real model-serving pressure.
The useful unit is the full inference path
Large-model inference is often limited by how weights, activations, and KV-cache data move through the system. That is why the conversation cannot stop at raw FLOPs. Precision format, memory bandwidth, and execution scheduling all matter.
- tensor cores accelerate the matrix-heavy sections of transformer inference
- HBM bandwidth matters because model serving repeatedly pulls large state through memory
- the cache and interconnect path become critical once batches and context grow
- LPX is useful here as a shorthand for the low-precision execution path needed to keep throughput practical
In other words, inference efficiency is a systems problem. The hardware architecture only pays off when the model stack, precision choice, and serving strategy are aligned with it.