[scottcha]

That is a pretty good article although the one factor not mentioned that we see that has a huge impact on energy is batch size but that would be hard to estimate with the data he has.

We've only launched to friends and family but I'll share this here since its relevant: we have a service which actually optimizes and measures the energy of your AI use: https://portal.neuralwatt.com if you want to check it out. We also have a tools repo we put together that shows some demonstrations of surfacing energy metadata in to your tools: https://github.com/neuralwatt/neuralwatt-tools/

Our underlying technology is really about OS level energy optimization and datacenter grid flexibility so if you are on the pay by KWHr plan you get additional value as we continue to roll new optimizations out.

DM me with your email and I'd be happy to add some additional credits to you.

[ccgibson]

To add a bit more to what @scottcha is saying: overall GPU load has a fairly significant impact on the energy per result. Energy per result is inversely related, since the idle TDP of these servers is significant the more the energy gets spread the more efficient the system becomes. I imagine Anthropic is able to harness that efficiency since I imagine their servers are far from idle :)

[Majromax]

You can infer the discount from the pricing of the batch API, which is presumably arranged for minimum inference costs. Anthropic offers a 50% discount there, which is consistent with other model providers.