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The break-even point is the workload and utilization level at which an owned or colocated GPU cluster costs no more than renting equivalent capacity for the same useful work. Calculate it by comparing fully loaded owned-cluster TCO with a rental bill over the same period, then divide by measured useful GPU-hours—not nameplate GPU-hours. Include power, cooling, facility fees, networking, people, financing, support, downtime and residual value; a bare cloud GPU rate is not a valid comparator.
What the break-even point actually answers
A GPU purchase can look inexpensive when you divide hardware price by theoretical GPU-hours. That calculation ignores idle capacity, failed jobs, electricity, cooling, rack space, engineers, financing and the cost of replacing an accelerator that becomes obsolete. Break-even analysis answers a narrower, practical question: at what sustained workload does owning provide the same or lower cost per unit of useful output than renting?
Use one horizon, one workload definition and one GPU class for both alternatives. If the workload is token generation, compare cost per million input and output tokens. If it is training, compare cost per completed training run or useful accelerator-hour. The comparison fails when an owned H100 cluster is measured against a cheaper rental instance with less memory, a different interconnect or materially different performance.
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Owned or colocated cluster TCO
For each month in the planning horizon, calculate:
Owned TCO = CapEx + financing/opportunity cost + electricity and cooling + colocation/facility + networking + staff and operations + support/maintenance + insurance + software + downtime/failure reserve − residual value
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Keep accounting depreciation separate from economic depreciation. A tax or book schedule can spread hardware cost over a fixed period even though resale value and technical usefulness fall faster when a new accelerator generation arrives. For an economic decision, include the cash invested, the financing cost and the resale value you realistically expect at the end of the horizon.
Rental TCO
Model the rental alternative as:
Rental TCO = comparable GPU or instance rate × purchased hours + VM host + storage + network + support − commitment or Spot savings + interruption and restart costs
Cloud providers add GPU charges to machine-type charges. Region, committed-use discounts, sustained-use discounts and Spot pricing can change the effective rate. Use the provider calculator or a billing export that includes the VM, attached storage, snapshots, inter-zone traffic, egress and any managed service used by the workload. A single on-demand GPU-hour copied from a pricing page is not an all-in rental price.
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Available capacity is the number of GPUs multiplied by the hours in the period. Useful capacity removes idle time and work that does not produce accepted output:
Useful GPU-hours = GPU count × period hours × measured productive utilization × goodput factor
Productive utilization should exclude queue time, data stalls, maintenance, failed jobs, preemption, restarts and time spent on experiments whose output is discarded. The goodput factor captures the remaining loss from retries, synchronization inefficiency and other overhead. For a monthly model, use the actual number of hours in each month or a documented convention such as 730 hours; do not mix a 730-hour owned model with a 720-hour rental model.
A cluster operating at 20% useful utilization carries nearly five times as much fixed-cost burden per productive hour as the same cluster at 100%. Show a range rather than one optimistic forecast. At minimum, test 20%, 40%, 60%, 80% and 95% useful utilization, and apply a goodput haircut in every case.
Build the owned-cluster cost model
Up-front capital and residual value
Include every item needed to produce the stated workload:
- GPUs, server chassis, CPUs, memory and local storage.
- High-speed networking, switches, cables and optics.
- Racks, power distribution, UPS equipment and installation.
- Deployment labor, burn-in, commissioning and software integration.
- Financing fees, interest and the opportunity cost of cash.
Subtract expected resale or residual value at the end of the planning horizon. State the assumed date and condition: a resale estimate for working, supported hardware is different from a liquidation value after a failure. OECD’s 2026 model separates GPU and installation CapEx from recurring costs; its classification places electricity, colocation, connectivity, engineering support, insurance and depreciation in recurring operating economics.
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Power, cooling and facility charges
Measure complete server or rack draw at the wall. Do not multiply GPU TDP by GPU count and call the result facility power; CPUs, memory, fans, storage, power supplies and networking also draw energy. Apply the facility’s power usage effectiveness (PUE) to IT load, then multiply by the local tariff:
Electricity cost = wall-measured IT load (kW) × PUE × hours × electricity price per kWh
In OECD’s illustrative H100 assumptions, each GPU draws 700 W and another 700 W is allocated to cooling, RAM and CPU at full capacity. With electricity at $0.25/kWh and PUE about 1.3, the resulting electricity assumption is about $300 per H100 per month. That is an illustrative scenario, not a quote; your measured draw, tariff, utilization and cooling design may differ.
Add charges that are often hidden in a server quote: rack or colocation fees, demand charges, backup generation, water or liquid-cooling fees and connectivity. The same OECD illustration assumes approximately $1,200 per H100 per month for colocation and about $500 per H100 per month for network bandwidth. Keep these as separate spreadsheet lines so a change in site or carrier is visible.
People, support and operational risk
Budget cluster engineering, monitoring, on-call coverage, orchestration, patching, security, insurance, spare parts and the capacity consumed by failures. A small deployment may use a fraction of an engineer; a larger deployment may require dedicated operations and facilities staff. OECD’s illustrative assumptions use one full-time engineer for the largest scenario, prorated for smaller deployments, plus 2% annual insurance and a reserve equal to 5% of GPU value for failures, redundancy, software and financing overhead.
Downtime is a cost even when no invoice appears. Estimate the GPU-hours lost to hardware faults, maintenance windows, queue imbalance and data pipeline outages. If a failed job must restart from an earlier checkpoint, include the duplicate compute and storage traffic in the downtime reserve or goodput factor, but do not count the same loss twice.
Normalize the rental comparator
Choose a rental instance with the same GPU memory, interconnect, host CPU and storage performance required by the workload. Record the region and date because rates change quickly. Include:
- GPU and machine-type charges for every purchased hour.
- Boot volumes, persistent disks, checkpoints and object-storage requests.
- Inter-zone traffic, public egress and data-transfer fees.
- Support plans, managed orchestration and observability services.
- Commitment discounts, sustained-use discounts or Spot prices only when your demand and interruption tolerance support them.
- Expected preemption, restart and queue delays for Spot or interruptible capacity.
For token workloads, convert both options to the same output measure. A rented instance that produces 20% more tokens per hour may be cheaper even at a higher hourly rate; conversely, a nominally cheap instance can lose its advantage when slower storage or network links reduce goodput.
Set up the spreadsheet
Use one monthly row per scenario. Keep physical measurements, prices and assumptions in an input area so they can be refreshed without rewriting formulas.
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| Column | What to enter |
|---|---|
| Available GPU-hours | GPU count × hours in the month |
| Useful GPU-hours | Available hours × measured utilization × goodput factor |
| Rental-equivalent cost | Useful work priced with the normalized rental comparator, including host, storage and network |
| Electricity | Wall draw × PUE × tariff × hours |
| Cooling and facility | Cooling charges, rack fees, demand charges and backup power |
| Colocation | Monthly space and facility fees |
| Network | Connectivity, transfer and egress costs |
| Staffing and support | Engineering, monitoring, software support and on-call allocation |
| Insurance and reserves | Insurance premium, spare parts, failures and downtime reserve |
| Financing | Interest, fees and opportunity cost of invested cash |
| Depreciation | Accounting view only; keep separate from economic cash cost |
| Residual value | Expected resale or salvage value in the final period |
| Cumulative cash flow | Owned cash outflow minus rental-equivalent cost, carried forward |
Graph cumulative owned cash flow against cumulative rental cost. The point where the owned line crosses the rental line is the cash break-even month. Keep a second graph for cost per useful GPU-hour; a project can reach cash payback while still costing more per useful hour after a later utilization decline.
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Calculate cost per useful GPU-hour
For the horizon, sum all owned costs, subtract residual value and divide by total useful GPU-hours:
Owned cost per useful GPU-hour = owned TCO ÷ useful GPU-hours
For rental, divide the fully loaded rental bill by the same useful-work measure. If one option measures useful hours after a 10% goodput loss and the other does not, the result is biased. Apply equivalent definitions, or translate both options into tokens, training steps, images, simulations or another output unit.
Solve for utilization break-even
Let F be owned fixed cost for the period, R the rental value per available GPU-hour, V owned variable cost per available GPU-hour, A available GPU-hours and u useful utilization on a common goodput basis. The simplified break-even equation is:
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Use the same useful-work adjustment in both R and V. If rental pricing is quoted per useful token rather than per hour, convert the rate using measured throughput before solving. If the denominator is zero or negative, ownership cannot break even under those assumptions: every additional hour is no cheaper than renting.
For a monthly model, fixed cost includes the capital recovery or financing burden assigned to that month, facility commitments, baseline staffing and other costs that continue while the cluster is idle. Variable cost includes electricity that changes with load, usage-based network charges and extra support. Do not put the full purchase price into every month’s fixed cost; place the cash purchase in month zero and use the cash-payback calculation for recovery.
Separate cash payback from accounting depreciation
Cash payback is the first month in which cumulative rental-equivalent savings exceed the upfront deployment cash:
Cash payback month = first month where cumulative (rental TCO − owned operating cash cost) ≥ upfront CapEx and deployment cash
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Depreciation answers an accounting or tax question, not whether the investment has recovered its cash. A five-year tax schedule can show expense after the hardware’s resale market has collapsed, while accelerated obsolescence can make economic depreciation much faster. Report both views when finance or tax teams need them, but do not substitute book depreciation for residual value and opportunity cost.
Worked calculation with clearly labeled assumptions
The following is illustrative arithmetic, not a current market quote. Suppose an eight-GPU cluster has 730 available hours per month, a measured useful utilization of 60% and a goodput factor of 0.90. Its useful capacity is:
8 × 730 × 0.60 × 0.90 = 3,153.6 useful GPU-hours per month
If the normalized rental alternative costs $4 per equivalent GPU-hour, its rental-equivalent compute value is about $12,614 per month before any additional storage, network or support charges. If the owned cluster’s measured electricity, colocation, network, staff, support and reserves total $21,000 per month, renting is cheaper at that utilization even before recovering CapEx. Increasing utilization to 95% raises useful capacity to 4,993.2 hours and rental-equivalent value to about $19,973; the owned operating cost still exceeds that illustrative rental value. The decision changes only if the owned variable cost falls, the comparable rental rate rises, the cluster produces more useful work per hour or fixed costs are spread across substantially more hours.
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Sanity checks from published scenarios
OECD 2026 token scenarios
OECD’s 2026 scenarios report that private hosting is not economical for workloads below 100 million tokens per month. In that model, private hosting reaches parity with cloud APIs after about 30 months at 1 billion tokens per month, about two months at 10 billion tokens per month and about one month at 50 billion tokens per month. These are scenario outputs based on stated assumptions, not universal thresholds. A different electricity tariff, accelerator, financing rate, utilization pattern, API price or model size can move the crossing point substantially.
NVIDIA’s 2023 illustration
NVIDIA’s October 2023 illustration uses 16,000 GPUs over four years, approximately $1 billion of infrastructure cost and approximately $2.5 billion of rental opportunity at $4 per GPU-hour. It labels a 15% utilization increase as worth more than $350 million. Treat this as a vendor illustration from October 2023, not a current market quote or an independent benchmark. Its lesson is directional: at very large scale, small sustained utilization changes multiply across many GPUs and years.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run sensitivity cases before committing
Create a matrix that varies the inputs most likely to change the result:
- Useful utilization: 20%, 40%, 60%, 80% and 95%.
- Goodput factor after restarts, queue inefficiency and data stalls.
- Electricity tariff, PUE and measured wall draw.
- Rental rate under on-demand, committed and interruption-tolerant pricing.
- GPU resale value and economic life.
- Financing rate and deployment delay.
- Colocation, bandwidth and egress charges.
- Failure rate, spare capacity and engineering staffing.
Mark the utilization at which ownership first beats each rental case. Then test a downside case in which demand arrives six months late, a GPU generation becomes obsolete early or the facility cannot deliver the promised power density. If ownership wins only in the optimistic column, treat the project as capacity insurance rather than a proven cost reduction.
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Choose buy, colocate or rent using more than price
Ownership is favored when
- Useful utilization is sustained and predictable across the hardware’s economic life.
- The workload needs persistent, high-throughput interconnects or data residency that rental capacity cannot reliably provide.
- Measured power, PUE and facility pricing are favorable.
- The organization can staff operations, maintain spares and absorb failures.
- Financing and residual-value assumptions remain acceptable in downside cases.
Rental is favored when
- Demand is bursty, experimental or difficult to forecast.
- Deployment speed and geographic flexibility matter more than lowest steady-state unit cost.
- Cloud commitments, Spot capacity or newer accelerators offer better economics than owned hardware.
- Power availability, cooling or data-center space would require a costly build-out.
- Operational, security or reliability requirements exceed the team’s in-house capability.
A hybrid plan can reduce risk
Many teams keep a predictable baseline on owned or colocated hardware and rent overflow, new GPU generations and short-lived experiments. Model the hybrid plan as two marginal curves: owned capacity has a fixed commitment and low incremental cost until full, while rental capacity begins immediately but scales with demand. Include data-transfer and scheduling overhead when work crosses the boundary.
Common calculation errors and fixes
Using GPU TDP as facility power
Error: multiplying the accelerator’s rated wattage by the GPU count. Fix: install a GPU server electricity usage monitor or read an accurate power meter at the rack, then apply PUE and the actual tariff.
Comparing a bare hourly rate with all-in ownership
Error: using only an on-demand GPU price. Fix: add VM host, storage, network, support, commitment conditions and interruption costs to the rental side.
Counting scheduled time as productive time
Error: assuming 100% utilization because jobs are queued. Fix: subtract idle, data stalls, maintenance, failures, preemption and restarts, then apply a goodput factor.
Mixing hardware classes or workloads
Error: comparing different memory sizes, interconnects or throughput. Fix: normalize to identical useful output, such as cost per million input and output tokens or cost per completed training run.
Hiding volatility in one average
Error: publishing one utilization, electricity price or resale value as if it were certain. Fix: show sensitivity cases and identify the variable that moves the break-even point most.
Calling depreciation a cash recovery schedule
Error: treating book depreciation as payback. Fix: track month-zero CapEx, monthly cash operating cost, financing and residual value separately.
Refresh inputs before relying on a result
Cloud prices, electricity, colocation, utilization, resale value and accelerator generations change quickly. Refresh the rates and measured power before publishing or approving a numerical case. The OECD and NVIDIA figures above are illustrative scenarios with stated dates and assumptions; they are not quotes for a particular cluster, region or workload.
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