This page shows a few different types of analyses of data centers. I have included different models that I have tried to make to understand and analyze data centers. The model is public and can be found on the open compute project (OCP). I began analysis of data centers by attempting to make the model a more reasonable tool where you can add different technologies and make a better analysis of total cost of operation. After attempting to work with the OCP model, I gave up on trying to modify the model and created a new model from scratch. The first model that I created was rather simple (part 4). The model attached to the fifth button below works through the IRR and the total cost of ownership in a more careful manner with drill down on cost of electricity components and a more detailed list of data centers. The final data center model also includes different ways to make scenario analysis. The models are all based on public data with the potential to adjust the data with specific configurations. I explain the process to arrive at the final model starting with the publically available model from the OCP.
The four different data center models are attached to buttons below. The first model has remarkable complex formulas and use of VLOOKUP and HLOOKUP that limit the ability to move things around and add scenarios to the model. The model did prompt analysis of different electricity equipment in a data center and the idea of computing total cost. In the second model I made changes to the OCP model and my intention was to make the more flexible, transparent and structured. The second model illustrates how you can fix and restructure a complex model like the public OCP model. In this model the different parts of a date center model are separated, the VLOOKUP and HLOOKUP are changed to XLOOKUP, the input configurations are changed, a financial model is added and the very detailed calculations with matrix algebra are put into a different sheet. In the end, I did not use this model because it was better to just start over with use of INPUTC and a more structured approach. The third model is a model I worked on that concentrated on
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Model 5: Model with Full Operating Data and Scenarios to Compute Total Cost of Operation
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Where to Find Data Center Models and Documentation on the Google Drive
The following two screen shots show some of the data that I have complied about data centers. The first screenshot shows where you can find the data under Chapter 1 and then under the featured models.
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The screenshot below shows how you can download the models from the google drive and other related data including financial reports.
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The remainder of this page describes four different models of different data centers.
Model 1: Open Compute Project Total Cost of Ownership Model
The file below can be found on the open compute project (OCP). With respect, this model does not follow reasonable modelling practices. Some of the areas where I think the model is not very helpful in analysis of detailed electric configurations nor provide an overview of what kinds of data centers produce overall prices. The model has some clear financial errors. Depreciation is included in cash flow; electricity purchases are made on a continual basis for back-up generation; the total cost of ownership does not divide by electricity used by IT, thereby evaluating efficiency. There are good things about the model and by opening the model you can find some good stuff. You can also look at how the indirect function is used with range names and see how matrix multiplication can be used (again, with respect, I think these are bad modelling practices.) The screenshot below shows where you can go to find the open compute project model.
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Ths screenshot below illustrates how the output from the model. The net present value of costs supposedly demonstrates the total electricity cost of the data center (not the total cost). It is not divided by a metric that represents how to evaluate the required price in terms of kW packets or in terms of tokens for the data center.
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In the next screenshots I show some screenshots from the model with excel formulas that may look impressive but can make the model very unflexible, untransparent, and unstructured. First,look at the HLOOKUP function combined with the INDIRECT function. The J4 is the thing to lookup. The indirect tells the table to lookup in. The false means it must be an exact match.
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The model contains a lot of complex equations. If you want to dissect this equation and the equation would have to be separated into many different formulas.
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Model 2: Revisions to Open Compute Model
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The second model is the revised OCP model. To revise the model I first found the VLOOKUP and HLOOKUP functions that used the INDIRECT function so I could move things around. Then I re-arranged the
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The calcualtion part of the model was unstructured where it was difficult to follow where the calculations were started and then move to electricity requirements.
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Redoing the Model with Classic Financial Modelling Techniques like InputC, timelines with flags, operating characteristics and cash flow
After considering trying to revise the model, I suggested to make a data center model in the context of what are now classic modelling principals including use of InputC with scenarios, use of the INDEX function for selecting scenarios, for development of time lines and flags. The notion of defining databases with different structures and putting units in the InputC is shown below. The database analysis that apply classic financial modelling techniques is attached to the button below. As this file contains macros, you need to unblock the macros after you download the file.
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The manner in which the InputC goes into the more detail is illustrated below. The input from InputC is shown in column F. The model is computed on a monthly basis so things like the date for replacement in months, the construction period in months and other factors can be computed precisely. Note that it is just as easy to make the model on a monthly basis.
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The data for capital expenditures, operating expenses, efficiency in PUE, efficiency in terms of tokens is taken by asking AI for the data and then putting the data in a database. This illustrates how AI can provide benchmarks for financial models. I am sure many of you could have done this more efficiently, but I was surprised about how different the results were when I change some small differences in prompts. To resolve this I made a sheet called a drill-down sheet where I included alternative data from different prompts.
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Detailed Data Center Model
The last data center model includes added data, added tools to store results and more modelling mechanics. In evaluating this model you can in theory address crucial questions about the true computational cost of AI and the question of whether the massive investment in data centres make economic sense. This model includes more detail on different data center configurations, degradation, efficiency statistics that are a function of temperature, drill down on the electricity costs and mechancial costs, better modelling of the capacity in terms of tokens and the utilisation factor of tokens, addition of InputS sheets
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Database in Model from AI prompts
In addressing the important question as to what is the true ultimate cost of using AI for things like coding that take a lot of tokens, the cost structure of the different data centers is a key issue. I tried to find the data from prompts to AI. But when I asked AI, I received very different responses to some key issues. The first problem was with the total capital expenditures per kW (I define this as kWit which is something like kWp versus kWac for solar, but I made this up). The screenshot below and the graph below that illustrate the difference in cost I that AI gave me from prompts that I thought were the same.
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A lot of the data that is extracted from the AI prompts is included in the model. I have used CNTL k with range names so you can see the source data. An illustration of the data on capital expenditures that illustrates different components of capital expenditures is shown in the screenshot below.
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A second example from the drill down page involves the PUE and efficiency. You can get data on the PUE and also on efficiency and/or losses from different activities as the PUE is the same as 1/efficiency. You can also acquire the PUE for different ambient temperatures. With the data on PUE for ambient temperatures, you can derive a linear (or more detailed) equation where the PUE changes depending on the temperature. The PUE examples are shown in the screenshot below.
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Financial Model, Cash Flow and Using Goal Seek to Find TCO
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With the InputC and the InputS, a financial model can be developed. Instead with wasting time with filling numbers into a template model as many do, I think it is better to start from scratch. You need to understand how the operation works and in particular how much electricity must be purchased relative to the IT load. It is a little confusing for people like me with background in electricity. In electricity there is one capactiy factor statistic that could be called the load factor. For the data center there is a load factor for purchasing electricity — it may be high, but probablity not 100%. The data centre must be cooled even if tokens are not sold.
It is important to distinguish the load factor from the utilization factor for a data centre. The ratio of million of tokens per kW can be stated in terms of maximum capacity like any hotel or other business. You can think of this as the total rooms that can be sold for a hotel. But this capacity does not mean that all of the tokens will be sold for 24 hours a day and seven days a week. One of the big issues for data centers can be overcapacity that just like it is the big issue for so many other things. The section of the model that works through efficiency and utilitsation factor is illustrated in the screenshot below.
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The operations data is used to compute net cash flows by considering the capital expenditures, the replacement expenditures and the operating expenses. The operating expense and the net cash flow is shown in the screenshot below. Note that at the bottom the NPV of free cash flow is computed at the target IRR. This allows derivation of the total cost of ownership.
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Scenarios and Presentation
The manner in which alternative scenarios are presented is shown below. You can select either to present the IRR for different alternatives or you can select to present the TCO. The different scenarios are created by clicking on the buttons.
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The goal seek macros used to create the scenarios are shown in the screenshot below.
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Future Enhancements
The model does not include taxes and financing. As with any model these should be added after the operations are established.