Tl;dr: Based on published data from DeepSeek, we can estimate it takes something like ~70 days of inference traffic (served by DeepSeek themselves, ignoring any other providers) to match the GPU hours used for the final training run for V3 and R1.
Simon Willison recently reshared some figures on inference costs for LLMs. I couldn't agree more with the comment further down that thread "The big AI labs continue to be infuriatingly opaque about the actual figures for their total electricity and water consumption".
A number of responses wonder about the cost of training. If you accept the reported figures for serving a query, what impact does it have if you amortise the energy spent training the model over the served queries? Mistral did this for their lifecycle analysis but they grouped together "training and inference" and kept confidential the ratio of energy for training vs inference by reporting a figure that combined the training cost with 18 months of usage. The thread reminded me of another datapoint available for DeepSeek that seemed worth writing up. I think this gives some helpful intuition for the amortised cost of training for a widely used model of that size, but to state the obvious any attempt to apply that intuition to other models is totally reliant on how widely used it is.
DeepSeek have published figures both on training and on inference for DeepSeek's website and API users. I will attempt to consistently refer to the figure for training as "final run training cost" to reflect the fact the number of GPU hours used in experimentation and failed attempts isn't reported. For final run training for DeepSeek-R1:
Now for inference, back in February DeepSeek wrote up details of their inference system giving details of cost of serving, profit margin, and load over a 24h period. So yes, we're extrapolating from this datapoint and assuming it's representative. Given the worldwide inference of DeepSeek R1/V3 is surely much larger (being openly licensed there are many vendors who are serving it), I'm not overly worried about this aspect. Their reported average inference serving infrastructure occupancy is 226.75 nodes (each node containing 8 H800 GPUs), meaning 43536 H800 GPU hours per day. At that rate, it will take ~67.5 days of traffic for the same number of H800 GPU hours to be used for inference as for the final training run.
All this to say, for a widely used model of DeepSeek R1 scale when looking at the cost of inference, accounting for the amortised final run training cost is more likely to be a multiplier of 2x or less rather than something much larger. In terms of energy, this does assume that the power draw of the H800 GPUs while running inference is similar to the draw during training. And to underline again, the reported training cost surely doesn't include experimentation, aborted runs etc.