AI Cloud TCO Model

The SemiAnalysis AI Cloud Total Cost of Ownership Model examines the ownership economics of AI Clouds that purchase accelerators and sell either bare metal or cloud GPU compute. It also sheds light on the likely future cost curves for AI Compute based on the capabilities of upcoming AI Accelerators as well as the impact of various optimization techniques and parallelism schemes being implemented in the market.

It can be used to evaluate the business case for establishing and running an AI Cloud for various stakeholders from AI Cloud management teams to equity and debt investors, examining the economics of business operations as well as AI Accelerator residual value. It can also serve as a useful benchmarking and planning tool for customers that are currently purchasing or are considering procuring AI Compute, particularly on a long-term basis.

The AI Cloud Total Cost of Ownership model incorporates the below topics and analyses:

  • Historical and future rental price analysis and estimates for a variety of GPUs incorporating the following:

    • Detailed install base by GPU projections through 2028, estimated GPU total unit shipments by major vendor through 2034.

    • Inference throughput, Training throughput, GPU TDP, All-in TDP per GPU, cost of ownership ($/hr), Inference Cost per M tokens, Training Cost per FLOP by accelerator including Nvidia, AMD, Intel, and custom accelerators.

    • Market-wide inference and training throughput, most advanced inference and training cost ($/M tokens), market average training cost ($/hr per PFLOP).

    • Analysis of impact of various optimizations and parallelism schemes (Pipeline Parallel, Tensor Parallel, Expert Parallel, Data Parallel) on GPU inference and training throughput.

    • Future GPU rental price scenario analysis based on supply-demand analysis and estimates and incorporating evolution of cost curve over time given future GPU capabilities.

  • GPU Total Cost of Ownership analysis, calculating comprehensive cost of operating GPU servers ($/hr) based on upfront server capex, system power consumption, colocation and electricity costs, costs of capital.

  • Returns and Residual value analysis including the following:

    • Net present value and residual value analysis for a GPU cluster based on future earnings and cash generation power.

    • Cumulative project and equity cash flow.

    • Equity and project IRR, return on assets, return on invested capital, return on equity, EBIT, EBITDA.

  • AI Cloud Full Financial Model incorporating the following elements:

    • Three statement financial model – Income Statement, Balance Sheet, Cash flow, including all key balance sheet items – server depreciation, unearned/prepaid revenue, borrowings and more.

    • Support for key financial assumptions: various capital structures and mix of debt/equity, mix of cash and PIK interest, accounting depreciation period, colocation, electricity, annual maintenance contracts, sales and marketing costs, customer fixed price and fixed price duration, customer prepay assumptions, physical GPU operating lifetime/endurance, repairs and maintenance, tax expense and more.

    • Overview of current market GPU rental prices and pricing variation.

    • LLM training and inference economics analysis, pricing trends and inference company profitability estimates.

The model will also include one year of quarterly updates for additional features and improvements, an initial call with SemiAnalysis to explain the model and methodologies employed, as well as subsequent ad-hoc calls to answer any questions that arise from the use of the models. Contact us at for more details.