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There is no universal token count at which a local GPU becomes cheaper than a hosted AI service. The crossover depends on your model, input/output mix, monthly and peak usage, how much of the local system sits idle, and the full cost of operating it. Calculate both options for the same workload and comparable model capability; otherwise, the cheaper figure may not describe a usable substitute.
What to compare before calculating
Start with the workload you actually expect, not a generic tokens-per-month threshold. Record monthly input tokens, output tokens and request count, along with peak concurrent requests and how demand is distributed over time. A workload spread evenly across the month can use an always-on system differently from one that arrives in short bursts.
Then select a hosted model and a local model that are genuinely comparable for your tasks. Check quality, context limits, memory fit, throughput, latency, availability and data-handling requirements. A smaller model that cannot meet your quality or response-time needs is not a like-for-like cost alternative, even if it runs cheaply.
For every option, note the model or hardware configuration, region, pricing mode and date you checked the price. For rented GPUs, also establish what the hourly rate includes, how billing is rounded, and whether the instance is billed while idle. Provider rates and availability can vary by configuration and location.
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How to calculate the monthly crossover
1. Calculate hosted inference cost
For a token-priced API, multiply monthly input tokens by the input rate and monthly output tokens by the output rate, using the rates for the selected model. If rates are listed per million tokens, divide each token total by one million before multiplying. Add any minimums or other billed components that apply.
For a rented GPU or dedicated instance, multiply its full hourly rate by the hours it will be billed. An instance kept running all month incurs billed idle time as well as active time; a service that can be stopped may have a different total. Include attached compute or storage charges if they are billed separately.
2. Calculate the full local-system cost
For a purchased system, spread the purchase cost over the useful life you assume, then add electricity, cooling, supporting CPU, RAM and storage, networking, space or colocation, maintenance and operations. Include financing, replacement risk and engineering or staff time when material. State your useful-life and utilization assumptions rather than treating a GPU’s purchase price as its monthly cost.
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Electricity is not just the GPU’s rated draw: consider the rest of the system and cooling. As one bounded illustration, the OECD’s 2026 report models one H100 at about 700 W at full capacity, with up to another 700 W for RAM, CPU and cooling. Its scenario assumes European electricity at about USD 0.25/kWh and a PUE of 1.3, yielding about USD 300 monthly electricity cost per H100. These are report assumptions, not a home-system estimate or a tariff for every region. OECD report
The same OECD scenario assumes colocation at approximately USD 1,200 per H100 GPU per month and models depreciation at 2% of original capital value per month. Those figures are scenario inputs, not generally applicable market prices or a universal useful-life rule. OECD report
3. Solve for the workload where totals meet
For a simplified calculation, let F be local fixed monthly cost, H the hosted marginal cost per comparable workload unit, and L the local marginal cost per unit. The crossover workload is:
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Break-even workload = F ÷ (H − L)
This expression only produces a positive crossover when hosted marginal cost exceeds local marginal cost. If the denominator is zero or negative, this simplified model has no positive break-even volume: local costs do not become lower merely by increasing workload. If costs vary with usage—for example, because you need another GPU at peak load—calculate monthly totals at realistic workload levels instead of relying on a single linear formula.
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For an API, marginal cost per unit depends on the input/output token mix and the selected model’s rates. For local inference, the unit must reflect the same useful work at acceptable quality and speed; GPU electricity alone is not the full cost. This formula is a decision aid, not a forecast, and it does not replace throughput and memory testing for the chosen model and serving stack.
How billing models change the answer
Hosted inference can be priced by input and output tokens, while dedicated GPU services and cloud instances may bill by GPU-hour or whole-instance time. Compare the unit that applies to your workload, rather than treating every hosted option as a per-token API. DigitalOcean lists serverless model prices and dedicated GPU-hour rates; Google Cloud documents GPU and accelerator-optimized machine pricing.
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| Published example | Rate or billing detail | How to interpret it |
|---|---|---|
| Google Cloud T4 GPU | USD 0.35 per GPU-hour on demand; USD 0.22 and USD 0.16 per GPU-hour for one-year and three-year commitments, respectively, on the pricing page inspected for this article on October 7, 2026. | Rates vary by region; Spot rates vary and may be discounted. Attached GPUs add to VM cost except on accelerator-optimized machine families whose pricing includes GPUs. Check the selected zone, machine and full VM cost. Google Cloud pricing |
| DigitalOcean dedicated GPU examples | H100 at USD 4.41/hour and H200 at USD 4.47/hour on the page last verified October 1, 2026. | These are page-specific prices, not a market average or a promise that a configuration is available to every account or location. DigitalOcean Inference pricing |
| Hugging Face Inference Endpoints | Hourly prices are listed for endpoint GPU instances; actual cost is calculated by the minute. | Check the specific provider, instance, memory and current availability. Hugging Face pricing |
| Lenovo Press cloud configuration examples | Its report lists GCP g4-standard-96 at USD 14.97/hour on demand and AWS p6-b200.48xlarge at USD 114.27/hour on demand. | These are unlike full configurations from publicly available official prices at the report’s time of writing, not directly comparable GPU-only rates or a provider ranking. Lenovo Press report |
Cloud headline prices can omit parts of the machine. Google Cloud says prices for GPU-attached accelerator-optimized machine families include the GPU, while attached GPUs on other configurations add to VM cost; verify the components included in the rate you use. Likewise, a GPU-hour rate and an API token rate are not directly comparable until you account for what each delivers, how long it is billed and how much capacity is available.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why utilization and operating work matter
An owned GPU incurs capital and support costs when demand is low, while a rented instance may be stopped or billed only for its runtime, depending on the service and configuration. Bursty usage can therefore favor a variable-cost service even when a local system has a lower marginal cost during active inference. Steady, sustained usage can improve the case for local hardware, but only if the system stays productive and meets service requirements.
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There is no universal break-even statistic in the OECD scenario. It models hosted API costs using Gemini 3.1 Flash at about USD 2 per million input tokens and USD 12 per million output tokens with a 40:60 input/output mix. Those are inputs to that report’s scenario, not a general current quote or an answer for another model and workload. OECD report
Use a decision table, not a token threshold
| Question | What to compare |
|---|---|
| What does it cost? | Total monthly cost at your actual workload, including fixed and usage-based charges. |
| What happens at average and peak demand? | Utilization, idle time, concurrency, capacity limits and cost when demand spikes. |
| Does it do equivalent work? | Model quality, memory fit, throughput and latency for your tasks. |
| Can it meet service needs? | Availability, response times and operational burden. |
| Where does data go, and what can you control? | Data-handling requirements and deployment control for each option. |
| What assumptions could change the result? | For cloud: region, machine components, billing granularity, commitment terms and Spot interruption risk. For local: purchase price, warranty, power draw, cooling, useful life, expandability and resale value. |
Recalculate when model choice, workload, region or prices change. If the local option appears cheaper, validate memory fit and measure throughput on the intended serving stack before treating the cost curve as an operational answer.
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