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GPU infrastructure has two different clocks: accounting depreciation spreads a server’s cost across an estimated useful life, while its resale value can fall much faster. A system can remain productive and earn revenue even after its market value has dropped sharply. For an investment decision, that means the depreciation schedule alone cannot tell you what the hardware will be worth at sale—or whether buying, leasing, or renting capacity is the better choice.
Why do GPU depreciation and resale value move on different timelines?
Depreciation is an accounting allocation of an asset’s cost over the period a company expects to use it. Resale value is what a buyer might pay for the equipment at a particular time. The first depends on an estimated useful life and the company’s accounting assumptions; the second depends on the market for that generation, its configuration, and the economics of operating it.
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Those measures can diverge substantially. CHG-MERIDIAN’s October 2026 estimates put an eight-GPU NVIDIA HGX B300 server at about 33% of its purchase price after two years and 27% after three years. By contrast, a six-year straight-line schedule would leave about 67% of original cost on the books after two years and 50% after three. These are different kinds of figures: the first are estimated market values, not guaranteed sale proceeds; the second are illustrative book values under a stated depreciation schedule.
The practical result is that “still useful” and “still valuable” are not synonyms. A server may continue handling paid workloads even when a buyer would offer far less for it than its accounting value. Conversely, a low book value does not mean the hardware has stopped contributing to revenue.
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How fast can AI GPUs lose resale value?
There is no single depreciation rate that applies to every GPU or server. The evidence points to rapid repricing as newer systems change the performance and power-efficiency buyers can get for each dollar.
New generations reset buyers’ comparisons
Annual architecture releases can create a repricing cliff. Buyers comparing systems look beyond whether a chip runs a workload: they weigh how much work it can complete per dollar and per watt. If a newer generation delivers more useful output for a similar operating budget, the older system may need a lower rental price or purchase price to remain attractive.
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McKinsey & Company’s 2025 analysis charts a 52% decline in A100 list price per GPU-hour from 2021 to 2025 and models GPU-hour prices falling by half or more over five years. The charted A100 list-price change and the broader modeled price erosion are not a promise about every provider’s realized rates. They do illustrate why a GPU’s revenue potential can be pressured even while it remains operational.
Operating efficiency affects what secondary buyers can pay
Power and cooling are part of the economics, not merely facility details. When a newer chip can deliver more work for the available power, an older system may be less attractive where power or cooling capacity is constrained. That can reduce both the pool of buyers and the price they can justify, even if the older GPU still performs its assigned tasks.
Rental markets can pull resale prices down
Used-GPU buyers often include rental providers. If those providers can charge less for capacity, their prospective revenue from an older system falls, which can lower their willingness to pay for it. J.P. Morgan Asset & Wealth Management’s 2026 outlook reports year-over-year declines of 21%–26% in selected A100 and H100 rental rates from November 2024 to December 2025. Those observations are specific to the selected rates and period; they should not be treated as a universal change across all regions, providers, or contracts.
Wear can matter, but technology age is usually a larger valuation driver. Configuration, memory, networking, inspection results, geography, and support status also affect how easily a system can be resold and what a buyer will offer.
Is a five- or six-year useful life realistic for AI servers?
A five- or six-year accounting life can be a reasonable estimate of how long an operator expects to use a server, but it is not a forecast that the system will retain most of its purchase price for that long. CHG-MERIDIAN’s 2026 review reports a five-to-six-year server useful-life range in major-company disclosures. That is an accounting estimate, not a resale guarantee or a claim that each operator will keep every system for the full period.
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The distinction matters when an investment model treats straight-line depreciation as a proxy for market value. Under a six-year straight-line schedule with no residual value, the expense is spread evenly across six years. That schedule says how cost is allocated in the books under those assumptions; it does not say how much a second-hand buyer will pay in year two or three.
Useful life depends on the workload and operating context
Hardware can remain commercially useful after newer generations arrive. NVIDIA says its A100 GPU shipped in 2020 and remains in commercial service six years later. Separately, CoreWeave has reported A100 capacity contracted into 2029. These examples show that older accelerators can still have workloads and customers; they do not establish a resale price, utilization level, or profit margin for a particular owner.
Whether an older system stays useful depends on what workloads it can serve, whether the software stack supports it, and whether customers will pay enough for its capacity. A long productive life can support the case for owning equipment, but it does not eliminate resale risk. The key question is not only “Can this GPU still run?” but also “Can it run the work we expect, at a price that covers its operating and financing costs?”
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If sale proceeds are below the asset’s book value, the difference is generally recognized as a disposal loss under the accounting treatment used. The sale can also crystallize a gap that had been present economically but was not yet reflected in the book value under the depreciation schedule.
CHG-MERIDIAN’s illustrative server example
CHG-MERIDIAN gives an example of a server bought for $420,000. After three years, its illustrative six-year straight-line book value is $210,000, while its estimated market value is about $114,000. Selling at that estimate would produce a $96,000 disposal loss: $210,000 book value minus $114,000 sale value. The market-value figure is an estimate, not a guaranteed offer, and actual proceeds can differ with the system and market conditions.
This example is useful because it separates three questions that are often collapsed into one:
- What did the company pay? The purchase cost establishes the amount to allocate over the asset’s accounting life.
- What is the book value now? That depends on the depreciation method, useful-life estimate, and any other relevant accounting adjustments.
- What can the company realize at sale? That depends on current buyer demand and the hardware’s resaleability, not just the accounting schedule.
A refresh decision should therefore compare the cost of keeping the old system with the full economics of replacing it. A refresh can improve performance or power efficiency, but an early sale may realize less than the remaining book value. The relevant investment question is whether the expected benefits of the new capacity justify the replacement cost and the loss recognized on disposal—not whether the old GPUs have reached zero book value.
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How should utilization affect a GPU investment decision?
Utilization is one of the largest determinants of whether expensive GPU capacity earns an adequate return. McKinsey & Company’s 2025 neocloud analysis says a small decline in rental prices or utilization below 80% can flatten returns in its modeled economics. That 80% figure is a modeled threshold, not a universal break-even point for every operator or contract.
High utilization spreads the cost of owned infrastructure across more productive hours. Low utilization leaves the owner paying for equipment, power, cooling, and financing while fewer billable hours generate revenue. But a forecast should not assume that all installed GPUs will be busy simply because demand for AI is growing: demand can vary by workload, customer, and period, and rental prices can change.
Concentration matters alongside the utilization percentage. If a large share of expected usage depends on a small number of customers or workloads, losing or delaying that demand can leave a costly system underused. Before buying, estimate utilization by realistic workload and customer scenarios, then test whether returns remain acceptable when both rental prices and busy hours are lower than planned.
Should an organization buy, lease, or rent GPU capacity?
Choose the structure that best matches expected use and the party best able to absorb the risk that the equipment will be worth less at the end. Buying can work when utilization is dependable and the operator expects to use the system through most of its productive life. Leasing can be attractive when a refresh in two to four years is likely and transferring residual-value risk has real value. Cloud or neocloud capacity can suit uncertain demand or a business that prioritizes avoiding underused hardware over owning an asset.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match| Approach | When it may fit | Main value to assess | Key exposure |
|---|---|---|---|
| Buy | Demand and utilization are reliable, and the operator expects to use the system through most of its productive life. | Whether utilization and workload revenue justify purchase, operation, and financing costs. | The owner bears resale uncertainty and may realize less than book value on an early refresh. |
| Lease | A refresh within two to four years is likely, or avoiding end-of-term resale exposure is important. | Whether the lease’s terms and cost are worth shifting residual-value risk. | Risk allocation depends on the lease structure; not every lease transfers the same risk. |
| Cloud or neocloud | Demand is uncertain, utilization may be low, or avoiding stranded hardware is more important than owning capacity. | Whether the cost and availability of rented capacity suit the workload and demand pattern. | Rates can change, and rented capacity does not provide ownership of a resaleable asset. |
A fair-market-value lease can leave the lessor with end-of-term value risk. That may reduce the lessee’s exposure to resale proceeds, but it does not make the economics disappear: the lease price and contract terms still matter. Compare the actual lease structure rather than assuming that every lease shifts residual risk in the same way.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should an investment model include?
A useful comparison evaluates the same workload and demand assumptions across ownership, leasing, and rented capacity. It should not treat a low purchase price or long accounting life as a complete investment case.
- Expected refresh date: Model the planned hold period and test what happens if replacement comes earlier than expected.
- Utilization and demand concentration: Estimate billable use under realistic scenarios, including lower utilization and delayed or lost demand.
- Performance per dollar and per watt: Compare the workload output and power demands of the system with the alternatives available to the organization.
- Power and cooling capacity: Account for whether the facility can support the hardware efficiently.
- Book-to-market gap: At each likely sale date, compare the estimated market proceeds with the projected book value instead of assuming they match.
- Financing cost: Include the cost of capital alongside hardware and operating costs.
- Software compatibility: Confirm that the intended workloads can run on the selected generation and configuration.
- Residual-value assumptions: Treat resale estimates as uncertain scenarios, not fixed proceeds.
- Exit liquidity: Consider whether there is a plausible buyer pool for that generation and configuration when the organization expects to exit.
Because the resale evidence is indicative, not guaranteed, scenario analysis is more useful than a single-point forecast. A downside case can test the effect of lower utilization, falling rental prices, or a weaker sale price; a base case can use the operator’s best-supported expectations. The purpose is to see whether the investment still works when the two clocks—book depreciation and market repricing—diverge.
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Can older A100 or H100 GPUs still make money?
They can remain commercially useful, but usefulness alone does not establish profitability. A100 capacity has evidence of continued commercial use: NVIDIA says the GPU remains in service six years after its 2020 launch, and CoreWeave has reported A100 capacity contracted into 2029. At the same time, J.P. Morgan’s selected rental-rate observations show that A100 and H100 rental rates fell 21%–26% year over year from November 2024 to December 2025.
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Those facts answer different questions. Continued service and contracts indicate that older GPUs can still find work. Rental-rate declines indicate pressure on the revenue available from some capacity. Neither tells an individual owner what its utilization, operating cost, financing cost, or net return will be. To assess a specific fleet, compare its achievable workload revenue with the costs of power, cooling, financing, support, and the opportunity cost of newer hardware.
Older GPUs are more compelling when there is reliable demand for their capabilities and the facility can operate them economically. They are less attractive when utilization depends on optimistic forecasts, power is constrained, or a newer system’s performance per dollar and per watt makes the old capacity hard to price competitively. Resale value should be assessed separately from continued ability to generate revenue.
What are the main mistakes to avoid?
- Equating book value with resale value. A depreciation schedule is not a market quote.
- Assuming a five- or six-year useful life protects residual value. The equipment can remain useful while its market price falls much sooner.
- Forecasting utilization without testing downside cases. McKinsey’s modeled neocloud returns are sensitive to utilization and rental prices.
- Treating one market observation as universal. Rental-rate data are period- and provider-specific, and equipment values vary by generation, configuration, geography, inspection, and market conditions.
- Refreshing without accounting for the exit. Compare the expected performance and operating benefits of new hardware with the likely sale proceeds and any disposal loss.
- Assuming every lease transfers residual risk. Risk allocation depends on contract structure; a fair-market-value lease is one example that can leave end-of-term value risk with the lessor.
The most defensible decision treats useful life, resale value, utilization, and refresh timing as separate inputs. That avoids confusing the ability to keep using a GPU with the ability to sell it at a favorable price.
Frequently Asked Questions
Does GPU depreciation mean the company has spent cash again each year?
No. Depreciation allocates an asset’s recorded cost across accounting periods; it is not a new payment to the hardware vendor each year. Cash flows occur through purchases, financing, and operating expenses, while depreciation affects how the asset’s cost is recognized in the accounts.
Can I use the accounting depreciation schedule for tax deductions?
Not necessarily. Financial-reporting useful lives and tax depreciation rules may differ, and tax treatment depends on jurisdiction and the company’s circumstances. Confirm the applicable treatment with a qualified tax professional rather than assuming a five- or six-year book life determines the tax deduction.
Why can two servers with the same GPU generation have different resale prices?
Generation is only one part of the buyer’s assessment. Memory, networking, form factor, configuration, inspection results, support status, and the market in which the equipment is sold can all affect resaleability and price.
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