Estimating power requirements

We calculated the peak power demand for each GPU cluster with the following formula:

Power = Chip TDP × number of chips × system overhead × PUE

We collected Thermal Design Power (TDP) for most chips when publicly available, though we did not find the TDP for some Chinese chips and custom silicon such as Google’s TPU v5p. We considered both primary and secondary chips when counting the number and types of chips. We used a 1.82× multiplier for non-GPU hardware to account for system overhead (additional power needed for other server components like CPUs, network switches, and storage), based on NVIDIA DGX H100 server specifications (NVIDIA, 2025). We also factored in Power Usage Effectiveness (PUE), which is the ratio of total data center power use to IT power use (with a minimum value of 1). According to the 2024 United States Data Center Energy Usage Report (Shehabi et al., 2024), specialized AI datacenter facilities had an average PUE of 1.14 in 2023, which is 0.29 lower than the overall national average of 1.43. We adjusted the trend for all datacenter facilities to estimate the average PUE of AI datacenters by subtracting 0.29 from the overall values reported by Shehabi et al. (Table 2).

For more information, see Appendix B.4 in our paper.

Estimating hardware cost

We use the publicly reported total hardware cost of the GPU cluster in our analysis whenever it is available. When it is unavailable, we estimate this cost based on the chip type, quantity, and public chip prices. For each type of chip, we multiply the cost per chip by the number of chips, multiply by factors for intra-server and inter-server overhead, and then sum these costs if there are multiple types of chips. Then, we adjust for the cost of server-to-server networking equipment, which was estimated to be 19% of final hardware acquisition costs. Finally, we apply a discount factor of 15% to the final hardware cost of the cluster to account for large purchasers of AI chips often negotiating a discount on their order.

Our final formula for estimating hardware cost is as follows:

Hardware acquisition cost = [(Primary AI chip cost × Primary AI chip quantity) + (Secondary AI chip cost × Secondary AI chip quantity)] × Intra-server overhead × Inter-server overhead × Discount factor

In this formula, our intra-server overhead, or “chip-to-server” factor, is 1.64×, our inter-server overhead, or “server-to-cluster” factor, is 1.23×, and our discount factor is 0.85×.

For more information, see Appendix B.5 in our paper.

Estimating certainty

In many cases, we don’t have all of the exact details for the system, and so have to make an estimate based on the information we have. When we do so, we explain our reasoning in the “Notes” field, and lower the “Certainty” field as warranted.

For example, when we know the power capacity of the system, but not the chip type, we sometimes estimate the FLOP/s based on the net FLOP/s/W based on the chip(s) they are most likely using. For systems several years in the future, where we don’t know the specifications for chips that will exist by then, we assume that the 1.26x/year trend in FLOP/s/W (Appendix D.1 of our paper) improvement will continue.