NVIDIA H200 SXM

Every number, explained

GPU memory

141 GB HBM3e

GPU memory is the workbench — the whole model must fit here or it doesn't run at all. Regular RAM cannot substitute: it sits on a link far too slow for the GPU, which would spend nearly all its time waiting instead of computing.

Memory bandwidth

4,800 GB/s TODO: verify

How fast the GPU re-reads the model from its own memory. To generate every single word, the GPU re-reads the ENTIRE model — all of it, every word. So faster memory means faster answers: double the bandwidth and the model talks roughly twice as fast.

Power draw

700 W — about 0.58 of one home's constant draw (a home draws ~1,200 W around the clock); one day (16.8 kWh) drains about 0.19 EV battery (an EV battery holds ~90 kWh).

Electricity burned every second the machine runs — this is a real monthly bill, not a spec-sheet footnote. We translate it below into homes and electric-car batteries so you can feel the size of it.

Form factor

SXM module (mounts on a GPU server board, 4–8 per server)

The CPU is the traffic director — it does almost none of the AI math. Don't overspend here; the GPU does the work.

Price

~$35,000

What it's for: 141 GB on one chip — the sweet spot for running 100B-class models without any clustering tricks.