The energy used for manufacturing the datacenters including the GPU's must also be rather high. Manufacturing is an energy-intensive sector.

Edit: I asked ChatGPT-5:

https://chatgpt.com/share/68e36c19-a9a8-800b-884e-48fafbe0ec...

it says:

>the manufacturing of GPUs and datacenters themselves consumes a large amount of energy, not just their operation. The operational energy use (for AI training, inference, cooling, etc.) gets most of the attention, but the embodied energy — the energy used to extract raw materials, manufacture chips and components, and construct facilities — is substantial.

and summarizes it with:

    4. Bottom Line

     • Manufacturing GPUs and datacenters is highly energy-intensive, but operational energy dominates over time.
     • For a single GPU, embodied energy ≈ 0.5–1 MWh.
     • For a datacenter, embodied energy ≈ 6–12 months of its operational energy.
     • Given AI-scale deployments (millions of GPUs), the embodied manufacturing energy already reaches terawatt-hours globally — roughly comparable to the annual electricity use of a small country.