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GPU infrastructure has two clocks: accounting depreciation spreads a system’s cost across its estimated useful life, while resale value can fall much faster as newer chips deliver more work per dollar and per watt. A GPU server may remain productive and generate revenue after much of its market value has disappeared. That gap affects whether it makes sense to buy, lease or rent capacity—and how much an early refresh may cost.
What is the difference between GPU depreciation and resale value?
Depreciation is an accounting estimate of how an asset’s cost is allocated over time. Resale value is what a buyer may actually pay for the hardware at a particular time. The two figures answer different questions: book value helps describe the asset in an accounting model, while market value helps estimate cash proceeds if the owner sells it.
For example, an operator might assign a server a six-year straight-line useful life. In that simplified model, its book value declines by one-sixth of its original cost each year, before considering any applicable accounting adjustments. The estimate does not guarantee that the server will retain one-sixth of its purchase price for every year, nor does it predict what a buyer will pay.
CHG-MERIDIAN’s October 2026 estimates illustrate the gap for an eight-GPU NVIDIA HGX B300 server. Its estimated market value is about 33% of purchase price after two years and 27% after three. Under a six-year straight-line schedule, book value would instead be about 67% and 50% at those points.
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| Time from purchase | Estimated market value of eight-GPU HGX B300 | Book value under a six-year straight-line example |
|---|---|---|
| Two years | About 33% of purchase price (CHG-MERIDIAN estimate, October 2026) | About 67% of purchase price |
| Three years | About 27% of purchase price (CHG-MERIDIAN estimate, October 2026) | About 50% of purchase price |
The book-value column is a mathematical illustration of six-year straight-line depreciation, not a claim that every operator uses that policy. Accounting lives are management estimates and can change. The B300 resale figures are estimates, not guaranteed offers; realized proceeds depend on the system’s configuration, condition, geography, support status and market conditions.
How quickly do GPUs lose value?
There is no single depreciation curve for every GPU or server. Market value can drop sharply when a new generation changes the performance-per-dollar and performance-per-watt comparison, even while the older equipment remains operational and useful.
Why new generations can reset prices
AI buyers compare more than whether a GPU can run a workload. They consider how much useful work it can deliver for the capital, electricity, cooling and operating capacity required. When a newer architecture improves that equation, customers may shift budgets and workloads toward the new hardware. That can put pressure on both used-sale prices and rental rates for older systems.
Power and cooling constraints make efficiency especially consequential. A data center with limited electrical or cooling capacity may prefer a newer system that performs more work within those constraints, even if an older GPU still runs the same software. That does not make the old GPU worthless; it narrows the set of buyers for whom its economics remain attractive.
Rental prices influence the used market
Secondhand buyers often include operators that rent GPU capacity to customers. If rental prices fall, those operators have less revenue available to cover hardware acquisition and operations, which can limit what they can pay for used systems. J.P. Morgan Asset & Wealth Management’s 2026 outlook compiles selected A100 and H100 rental observations showing year-over-year rate declines of 21%–26% from November 2024 to December 2025. Those are selected provider observations over a defined period, not a universal price forecast for every GPU rental market.
McKinsey & Company charted a 52% decline in A100 list price per GPU-hour between 2021 and 2025. Its analysis also models GPU-hour prices falling by half or more over five years. A list-price trend or modeled decline is not necessarily the realized rate for every customer: workload, contract terms, availability and provider all matter.
Wear is only part of the valuation
Physical condition matters, but technology age is often the larger driver of valuation. Configuration, memory and networking can affect whether a system fits a buyer’s workloads and facilities. A working GPU may therefore have limited resale appeal if its system design, memory profile, interconnect or support situation does not fit likely buyers.
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Can older A100 or H100 GPUs still earn money?
Yes. A GPU can continue generating revenue after its resale value has fallen, provided the workload, customer demand and operating costs still make the system useful. Productive life and resale life are not the same thing.
A100 GPUs launched in 2020 and remain commercially useful. NVIDIA says the A100 shipped in 2020 and is still in commercial service six years later. That is a vendor statement about continued service, not proof that every A100 installation is profitable. Separately, CoreWeave has reported A100 capacity contracted into 2029, evidence that at least some customers continue to commit to this generation for specific use cases.
Older GPUs may retain a role in inference, workloads that do not require the latest hardware, or markets where a lower acquisition cost makes them viable. J.P. Morgan Asset & Wealth Management’s Michael Cembalest has noted that processors can be repurposed for inference or resold to emerging countries. Neither path guarantees a buyer or a particular price; it illustrates why a generation change does not automatically make all older capacity unusable.
To assess an older fleet, estimate the net cash it can earn while it remains in service rather than treating its original purchase price or current book value as proof of profitability. The relevant question is whether revenue or internal value exceeds the costs of power, cooling, support, financing and operating the system, compared with the next-best way to obtain the same capacity.
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Why can a profitable GPU server still create a disposal loss?
An early sale can crystallize a loss when market value is below book value. The accounting loss is the difference between the recorded book value at disposal and the sale proceeds, subject to the organization’s accounting treatment.
CHG-MERIDIAN gives an illustrative example of a server purchased for $420,000. After three years, its book value under the example is $210,000, while estimated market value is $114,000. Selling at that estimated market value would create a $96,000 disposal loss relative to book value.
That loss does not by itself show that the server was a bad investment. The asset may have earned revenue or supported business operations during the first three years. Conversely, a smaller accounting loss does not make continued operation economically sound if power, maintenance or opportunity costs exceed the value of its output. Separate the historical accounting result from the forward-looking choice to keep, sell, replace or lease.
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Is a five- or six-year useful life realistic for AI servers?
It can be realistic as an accounting estimate or as a measure of how long hardware remains useful, but it should not be treated as a forecast of market value or as a promise that the same configuration will remain the best choice for six years.
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Operationally, the A100’s continued commercial service six years after launch and CoreWeave’s reported A100 capacity contracted into 2029 show that older generations can stay in use. Those examples establish longevity, not equal performance, equal rental rates or equal profitability across generations.
Use separate assumptions in a financial model for:
- Accounting life: the period over which the organization expects to allocate the asset’s cost.
- Productive life: how long the equipment can deliver useful work for the operator or its customers.
- Resale life: the period during which likely buyers will pay enough to make a sale worthwhile.
Keeping these assumptions distinct helps avoid a common planning error: assuming that a server depreciated over six years will also preserve a substantial share of its purchase price for six years.
Should you buy, lease or rent GPU capacity?
The choice depends on how predictable demand is, when a refresh is likely and who bears the risk that hardware will be worth less than expected at the end of its use. Compare the alternatives against the same workload and expected time horizon, rather than choosing from the monthly payment alone.
| Choice | When it may fit | Main exposure to assess |
|---|---|---|
| Buy | Expected use extends through most of the productive life, utilization is reliable, and the operator can absorb resale uncertainty. | Capital tied up in the hardware, financing cost, and the gap between book value and sale proceeds if the system is refreshed early. |
| Lease | A refresh is likely in two to four years and transferring some end-of-term residual risk is valuable. | Lease terms and cost, including who bears residual-value risk and what the contract requires at return or renewal. |
| Cloud or neocloud capacity | Demand is uncertain, expected utilization may be low, or avoiding stranded hardware matters more than owning equipment. | Rental-price changes, availability, contract terms and the economics of the expected workload over time. |
A fair-market-value lease can leave the lessor with end-of-term value risk. The exact allocation depends on the contract; read the return, purchase, renewal and end-of-term provisions rather than assuming all leases transfer the same risk. Leasing can shift residual exposure, but it does not eliminate the need to compare total cost with expected use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you evaluate a GPU infrastructure investment?
Model the operating case and the exit case together. A purchase can look attractive at high utilization but fail if demand falls; a conservative resale assumption can matter more when the system is likely to be replaced early.
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- Set the refresh date. Estimate when performance, power efficiency, software needs or customer demand may justify replacement. Model more than one date if the timing is uncertain.
- Estimate utilization and demand concentration. Distinguish contracted or recurring demand from forecasts, and consider whether a small number of customers account for most use.
- Check power and cooling capacity. Include whether the equipment can be operated within the site’s available infrastructure and what competing use that capacity may have.
- Compare performance per dollar and per watt. Assess the useful workload delivered by the current system against replacement hardware and rented capacity, not just raw GPU counts.
- Build the book-to-market gap into the exit case. Estimate sale proceeds separately from scheduled book value, and test what an early refresh would do to the disposal result.
- Include financing and lease terms. Compare funding costs and, for leases, identify who carries the residual-value risk and what obligations remain at the end of the term.
- Test software compatibility and exit liquidity. Confirm that workloads can run on the planned hardware and consider how configuration, memory, networking, geography and support status affect the pool of potential buyers.
Utilization deserves particular scrutiny in rental businesses. McKinsey & Company’s 2025 neocloud analysis says returns can flatten with a small rental-price decline or utilization below 80%. Treat 80% as a modeled threshold in that analysis, not a guaranteed break-even point for every operator. Financing, power costs, prices, workload mix and other assumptions change the outcome.
What assumptions should you stress-test?
A single forecast hides the fact that GPU economics can change through several connected channels. Use scenarios to see which assumption actually determines whether ownership, a lease or rented capacity is preferable.
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- Earlier refresh: What if the next system becomes compelling before the planned accounting life ends? Recalculate the sale proceeds and any book-to-market disposal gap.
- Lower rental prices: Test the effect of prices falling faster than expected, particularly if the business depends on reselling GPU hours.
- Lower utilization: Model demand below the base case, including periods when hardware is available but not earning revenue.
- Weak resale liquidity: Do not assume an estimated market value guarantees a buyer. Test lower proceeds or a delayed sale.
- Different workload fit: Check whether older hardware can be reassigned to inference or another workload, and whether the software and customer requirements support that plan.
- Facility constraints: Compare the value of using power and cooling capacity for older GPUs with the alternative of allocating that capacity to more efficient equipment.
Resale estimates are especially sensitive to generation, memory, form factor, geography, inspection, support status and market conditions. Keep those factors visible in the model instead of applying a single percentage to every server in a fleet.
Frequently Asked Questions
Does GPU depreciation reduce taxes in the same way in every country?
No. Tax depreciation rules depend on jurisdiction and the taxpayer’s circumstances, and they may differ from the useful life used in financial reporting. Confirm the applicable treatment with a qualified tax adviser rather than using a book-depreciation schedule as a tax answer.
Can I use an estimated GPU resale percentage as a guaranteed trade-in offer?
No. CHG-MERIDIAN describes its B300 figures as estimates, and realized proceeds depend on system details and market conditions. Obtain a current, configuration-specific offer before treating a sale value as cash available to fund a refresh.
Does a GPU server need to be fully depreciated before I can replace it?
No. Depreciation is an accounting allocation and does not require an operator to keep hardware until its book value reaches zero. The decision to replace it should weigh future operating economics and the consequences of selling below book value.
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Memory, networking, form factor, configuration, condition, support status and location can change how well a system fits a buyer’s needs. A generation-level price estimate cannot account for every one of those differences.
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