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Neither cloud GPUs nor owned hardware is always cheaper. Cloud rental usually wins when demand is irregular, uncertain, short-lived, or needs capacity you cannot procure and operate quickly. Buying can cost less when a suitable system stays productively busy for long periods and its capital, power, cooling, maintenance, facility, and staffing costs are lower than the equivalent cloud capacity. The reliable way to choose is to model your actual workload and time horizon—not to compare a cloud GPU-hour headline with a server purchase price.
For inference, compare the cost of producing a defined amount of useful output, such as one million tokens at a specified model, precision, quality, and latency. For training, compare the cost of a completed run or experiment, including idle time, storage, data transfer, interruptions, and capacity constraints.
Start with the workload, not the hardware
A cost comparison is meaningful only after you define what the system must do. Record these inputs before looking at prices:
- Workload type: inference, fine-tuning, pretraining, batch analytics, or a mixture.
- Model and precision: model size, quantization or floating-point format, and required GPU memory.
- Performance target: requests or tokens per second, batch size, and maximum acceptable latency.
- Availability: whether interruptions are acceptable and whether capacity must be available at a particular time.
- Operating schedule: continuous use, business hours, scheduled bursts, or occasional experiments.
- Data constraints: storage, network transfer, data residency, and security requirements.
- Planning horizon: the months or years over which hardware will be used, financed, depreciated, or replaced.
Use the same performance and service target for every option. A cheaper GPU that cannot hold the model, meet latency requirements, or finish a training run in the available window is not an equivalent alternative.
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How cloud GPU pricing actually works
Cloud providers sell GPU capacity through several purchasing models. Each changes both the price and the risk that capacity will be unavailable or that you will pay when the workload is idle. AWS describes the models and their terms in its EC2 billing and purchasing options documentation.
| Cloud option | How it works | Best fit | Main economic risk |
|---|---|---|---|
| On-Demand | Pay for capacity as used; AWS states EC2 On-Demand is billed by the second. | Uncertain demand, prototypes, short jobs, and workloads that must start without a long commitment. | Highest hourly rate and a potentially large bill if instances remain running while idle. |
| Savings Plans | Commit to a specified level of spend for one or three years in exchange for discounted rates. | Predictable baseline usage that can be kept busy throughout the commitment. | The commitment creates a cost floor even when demand falls or the workload moves elsewhere. |
| Reserved Instances | One- or three-year reservations can reduce the rate for eligible configurations. | Stable, known instance usage with limited changes in region or configuration. | Reduced flexibility and possible underutilization if requirements change. |
| Spot Instances | Use spare capacity at a variable discount; AWS advertises discounts of up to 90% versus On-Demand. | Checkpointed training, fault-tolerant batch work, and experiments that can wait or restart. | Capacity can be interrupted, and the advertised maximum discount is not a guaranteed rate for a particular GPU, region, or date. |
| Capacity Blocks | Reserve GPU instances in advance for a defined future period. | Planned training events that need assured capacity at a specific time. | You pay for a reservation and must schedule accurately; flexibility is lower than On-Demand. |
Current rates vary by GPU family, region, operating system, and availability. Check the exact configuration in the AWS EC2 pricing pages or calculator before making a business case. A reserved or Spot rate from one region cannot be transferred to another workload or date.
What ownership costs beyond the purchase price
An owned workstation or server turns an hourly cloud bill into a capital and operating-cost problem. Include every cost required to deliver the same useful work:
- Acquisition or financing: server, GPUs, CPUs, memory, storage, networking, chassis, power supplies, and any financing interest.
- Useful life and residual value: how many years the system remains suitable and what value, if any, remains when it is replaced.
- Maintenance: warranty extensions, replacement parts, support contracts, repairs, and administrator time.
- Electricity and cooling: the server’s draw plus cooling overhead. A GPU’s board rating is not the facility’s complete power bill.
- Facility or colocation: rack space, power delivery, connectivity, physical security, and remote-hands charges.
- Operations: monitoring, patching, orchestration, backups, incident response, and staff training.
- Underutilization: capital and facility costs continue when the GPUs are idle.
- Upgrade and reliability costs: replacement cycles, spare capacity, and the effect of a hardware failure on service-level commitments.
Lenovo’s 2026 total-cost analysis includes these categories in its scenarios, but its figures are vendor-published examples based on specific systems, operating assumptions, and public cloud prices. They are not a market-wide quote or a prediction of your actual bill.
Published 2026 examples: what the numbers do—and do not—show
The following acquisition prices are Lenovo’s listed prices, with the price input dated June 15, 2026. Each is a particular configuration, not a generic price for that GPU family.
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| Configuration | Listed system price | Source qualification |
|---|---|---|
| Config A: 2 × RTX PRO 6000 | $68,010.96 | Lenovo 2026 scenario; price input June 15, 2026 |
| Config B: 8 × H200 | $397,801.60 | Lenovo 2026 scenario; price input June 15, 2026 |
| Config C: 8 × B200 | $550,475.10 | Lenovo 2026 scenario; price input June 15, 2026 |
| Config D: 8 × B300 | $785,606.50 | Lenovo 2026 scenario; price input June 15, 2026 |
| Config E: 4 × L40S | $113,186.50 | Lenovo 2026 scenario; price input June 15, 2026 |
Lenovo models an 8-GPU H200 system at $9.80 per operating hour: $5.45 for amortized maintenance, $2.27 for power and cooling, and $2.08 for colocation. Those values are configuration- and location-specific, so substitute your own electricity, facility, and support rates.
The same example against cloud rates
For an Azure ND96isr H200 v5 instance, Lenovo’s dated table lists these hourly rates:
| Purchase model | Hourly rate in Lenovo’s table | Term or qualification |
|---|---|---|
| On-Demand | $114.65 | Lenovo 2026 table |
| One-year reserved | $73.39 | Lenovo 2026 table |
| Three-year reserved | $50.33 | Lenovo 2026 table |
| Five-year reserved | $46.56 | Lenovo 2026 table |
Using its own $397,801.60 system price and $9.80 hourly operating estimate, Lenovo calculates break-even at approximately 3,793 hours (about 5.2 months of continuous operation) against its On-Demand rate, approximately 9,800 hours (about 13.4 months) against its three-year reserved rate, and approximately 10,800 hours (about 14.8 months) against its five-year reserved rate. These are outputs of Lenovo’s assumptions, not universal thresholds: they depend on the listed hardware price, the $9.80 operating cost, the cloud rates, utilization, and the treatment of financing, depreciation, downtime, and residual value.
Lenovo also lists AWS p6-b200.48xlarge at $114.27 per hour On-Demand, $91.24 for one-year reserved, and $85.05 for three-year reserved. Its p6-b300.48xlarge figures are $142.75 On-Demand, $113.98 for one-year reserved, and $106.26 for three-year reserved. These are dated table entries, so they should be rechecked before use in a current budget.
Inference: measure cost per useful output
An hourly GPU rate says nothing about how much work the GPU completes. Two systems with the same hourly price can have very different throughput, and a faster system may produce more output before it is stopped.
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Define a comparable unit such as cost per million generated tokens. Hold constant:
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- the model and version;
- precision or quantization;
- prompt and output-token mix;
- batching and concurrency;
- quality target and any acceptance or retry rate;
- latency or service-level objective; and
- realistic utilization, including warm-up, queueing, maintenance, and idle periods.
Then calculate:
cost per useful token = (GPU cost + storage, network, power, facility, and operations cost) ÷ accepted output tokens
For a cloud deployment, use the selected On-Demand, commitment, Spot, or Capacity Block price and include storage and data transfer where they apply. For owned equipment, include amortization or financing, maintenance, electricity, cooling, facility, staffing, and the same utilization assumptions. NVIDIA presents throughput and cost per token as the central inference comparison. That framing is useful, but claims that a particular product has the lowest token cost are vendor claims rather than independent proof.
Training and fine-tuning: price the completed run
Training economics are dominated by whether the job finishes reliably and how often it must restart. Compare the cost of a completed run or experiment, not merely the nominal GPU-hour:
- Estimate total GPU-hours at the required model size, precision, batch size, and parallelism.
- Add CPU, memory, local storage, object storage, checkpoint storage, and data-transfer costs.
- Model queue time and startup time. A low hourly rate is not useful if capacity is unavailable during the project window.
- For Spot capacity, estimate interruption frequency, checkpoint overhead, restart time, and the probability of obtaining replacement capacity.
- For owned hardware, include the fraction of time reserved for experiments that are not running and the cost of a hardware failure.
- Divide total spend by completed runs or accepted experiments, not by the number of attempted launches.
Cloud capacity is especially valuable for one-off training, uncertain research, and bursts that would leave a purchased cluster idle. A local cluster can be attractive when the same configuration runs continuously and data movement or residency makes external processing expensive or impractical.
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Utilization is the break-even lever
A purchased server incurs capital, facility, and often maintenance costs whether it is running at 10% or 100% utilization. An On-Demand instance can be stopped when unused, but its rate is higher while active. Commitment pricing lowers the rate only by accepting a usage obligation; unused commitment is still a cost.
Build at least three utilization scenarios:
| Scenario | Cloud assumption | Ownership assumption | Question it answers |
|---|---|---|---|
| Low or irregular | On-Demand or occasional Spot; instances stop between jobs. | Server is idle much of the time but still depreciates and occupies space. | Does flexibility outweigh the owned system’s idle cost? |
| Predictable baseline | One- or three-year commitment sized to the steady workload. | System runs most days with planned maintenance. | Does the committed cloud discount beat ownership after operations? |
| High and sustained | Reserved or committed capacity, with enough demand to use it. | High utilization and a defined replacement horizon. | Does local control and output volume justify capital and operational responsibility? |
Do not use a single break-even month as a decision rule. Test what happens if demand is half the forecast, the hardware arrives late, the model changes memory requirements, electricity costs rise, or a commitment cannot be resized.
A practical comparison procedure
- Write the service target. Specify model, precision, memory, throughput, latency, availability, and data location.
- Choose equivalent capacity. Confirm that each cloud instance and owned configuration can actually meet the target.
- Price cloud alternatives separately. Calculate On-Demand, commitment, and interruptible scenarios; do not average them into one rate.
- Build the ownership total. Add acquisition or financing, useful life, maintenance, power, cooling, facility or colocation, operations, software, and residual value assumptions.
- Apply realistic utilization. Include idle hours, queued jobs, maintenance windows, failed runs, and demand variability.
- Normalize the result. Use cost per useful token for inference and cost per completed run or experiment for training.
- Stress-test the decision. Recalculate with lower utilization, higher power prices, delayed procurement, interruptions, and a shorter hardware life.
- Include the non-price constraints. Evaluate data residency, security, staffing, reliability, upgrade speed, and guaranteed access to capacity.
When cloud GPUs are usually the better choice
- Demand is seasonal, experimental, or difficult to forecast.
- You need a large cluster for a short training event.
- You cannot install adequate power, cooling, networking, or physical security.
- Your team lacks the operational capacity to maintain GPU servers.
- You need to change GPU families quickly as models or memory requirements evolve.
- Interruptible training can use Spot capacity with robust checkpointing.
- Data and compliance requirements permit cloud processing and transfer costs are manageable.
When buying can be economically and operationally sensible
- A known inference or training workload will keep the system busy for most of its useful life.
- Data must remain local or moving it repeatedly would be costly or risky.
- You have reliable power, cooling, networking, facility space, and support expertise.
- Latency or availability requirements favor a dedicated system.
- The required GPU configuration is stable and can be kept productive through the replacement horizon.
- You can finance the purchase without making future upgrades or other projects unaffordable.
Common mistakes that invalidate the comparison
Comparing a server price with a cloud GPU-hour
A purchase price excludes power, cooling, maintenance, facility, operations, financing, and idle time. Add those costs before calculating a break-even point.
Using peak throughput as everyday throughput
Inference throughput changes with sequence length, batching, concurrency, precision, and latency targets. Use delivered, accepted output under the service level you actually promise.
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AWS’s “up to 90%” statement is a maximum advertised discount. It is not a guaranteed price, availability level, or interruption rate for your GPU and region.
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Assuming a reservation is free flexibility
One- and three-year Savings Plans or Reserved Instances reduce rates in exchange for commitment. A five-year figure in a vendor comparison may not match the term or availability of your actual service.
Ignoring procurement and deployment time
Owned capacity may require server lead time, rack work, electrical upgrades, network changes, acceptance testing, and software deployment. Include the value of being able to start a cloud job immediately.
Accepting a vendor break-even claim as universal
Lenovo’s 2026 examples are useful illustrations, but they use its configurations, prices, operating assumptions, and dated cloud rates. Rebuild the calculation with your quotes and workload.
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A decision rule you can defend
Choose cloud capacity when flexibility, speed, burst access, or avoided operational work is worth the premium, or when utilization is too uncertain to support ownership. Choose owned hardware when a defined workload can keep a suitable system productive and the fully loaded cost per useful output remains lower after stress-testing utilization, energy, facility, staffing, and replacement assumptions.
Document the assumptions beside every result: date, region, GPU model, purchase model, utilization, service target, power rate, facility rate, useful life, and whether prices are recurring, promotional, or vendor-specific. Revisit the model when the workload, model, GPU generation, or cloud terms change.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
