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The GPU-hours stat here allows us to back out some interesting figures around electricity usage and carbon emissions if we make a few assumptions. 2,788,000 GP
by Centigonal 2y ago
The GPU-hours stat here allows us to back out some interesting figures around electricity usage and carbon emissions if we make a few assumptions.
2,788,000 GPU-hours * 350W TDP of H800 = 975,800,000 GPU Watt-hours
975,800,000 GPU Wh * (1.2 to account for non-GPU hardware) * (1.3 PUE [1]) = 1,522,248,000 Total Wh, or 1,522,248 kWh to train DeepSeek-V3
(1,522,248 kWh) * (0.582kg CO2eq/kWh in China [2]) = 885,948 kg CO2 equivalents to train DeepSeek-V3
A typical US passenger vehicle emits about 4.6 metric tons of CO2 per year. [3]
885,948 kg CO2 per DeepSeek / 4,600 kg CO2 per car = 192.6 cars per DeepSeek
So, the final training run for DeepSeek-V3 emitted as much greenhouse gasses as would be emitted from running about 193 more cars on the road for a year.
I also did some more math and found that this training run used about as much electricity as 141 US households would use over the course of a year. [4]
[1] https://enviliance.com/regions/east-asia/cn/report_10060 https://enviliance.com/regions/east-asia/cn/report_10060
[2] https://ourworldindata.org/grapher/carbon-intensity-electricity https://ourworldindata.org/grapher/carbon-intensity-electric...
[3] https://www.epa.gov/greenvehicles/greenhouse-gas-emissions-typical-passenger-vehicle https://www.epa.gov/greenvehicles/greenhouse-gas-emissions-t...
[4] divided total kWh by the value here: https://www.eia.gov/tools/faqs/faq.php?id=97&t=3 https://www.eia.gov/tools/faqs/faq.php?id=97&t=3
- hugs 2y agothe nice thing about ai's energy usage is that no one complains about bitcoin's energy usage anymore. (i'm kidding, people still complain.)
- Centigonal 2y agoActually -- and this is insane -- the amount of electricity required to train DeepSeek-V3 would power the Bitcoin network for all of 5 minutes. DeepSeek would have to fully train a brand new V3 every week to approach the kinds of power consumption numbers that individual bitcoin mining facilities are doing. The energy use from BTC is ludicrous. (I'm assuming 155 TWh/yr for Bitcoin, using the low-end estimate from here: https://www.polytechnique-insights.com/en/columns/energy/bitcoin-electricity-consumption-comparable-to-that-of-poland/ https://www.polytechnique-insights.com/en/columns/energy/bit... )
- amelius 2y agoIf the energy of Bitcoin was diverted to AI, we would have AGI now. Maybe we should be thankful.
- jononor 2y agoSatoshi is Sarah Connor?? That explains.
- GTP 2y agoThere's nothing backing this claim.
- mystified5016 2y agoWhoosh
- patapong 2y agoOr, the equivalent of around 3 flights between the UK and Japan (297,926kg [0]). [0] https://skift.com/2024/11/06/co2-setback-as-emissions-on-uk-japan-air-routes-surge-by-21/ https://skift.com/2024/11/06/co2-setback-as-emissions-on-uk-...
- pogue 2y agoAre the stats from training ChatGPT, Claude or other models public? It would be interesting to see a comparison to them.
- Centigonal 2y agoThey mostly aren't. The lack of transparency around how many parameters frontier models have and how long they're trained is a big obstacle when it comes to estimating the energy impact of training very large models. A group at Stanford has been benchmarking model providers by transparency here: https://crfm.stanford.edu/fmti/May-2024/index.html https://crfm.stanford.edu/fmti/May-2024/index.html I think a great way to create positive change in the world is to pressure OpenAI, Anthropic, Google, XAI, and Meta to all share details about the energy cost of training and inference for their models. If every major provider provided this transparency, it would be less valuable to keep that info secret from a "keep your competitors in the dark" perspective. It would also allow customers to make decisions based on more than just performance and cost.