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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesThere is no universally cheaper accelerator for AI inference. Compare the cost of delivering the same useful output at the same quality and latency—not peak chip specifications or hourly rental prices alone. Measure throughput on your workload, calculate the full cost of keeping that throughput available, and account for how much of the paid capacity is productively used.
Start with cost per useful output, not the chip’s advertised speed
An accelerator’s peak performance does not tell you what it will cost to serve a particular model. Real cost depends on the model and serving stack, the input and output mix, batching, concurrency, latency target, utilization, and the complete cost of operating the system. A GPU can have a higher hourly price and still cost less per token if it delivers sufficiently more useful throughput. A specialized chip can have a lower hourly price and still lose if it serves the workload less efficiently or requires additional engineering.
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NVIDIA’s June 17, 2026 total-cost-of-ownership guidance gives this simplified compute-cost calculation:
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Cost per million tokens = hourly accelerator cost ÷ (measured tokens per second × 3,600) × 1,000,000.
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Use a measured throughput that reflects the model and serving configuration you intend to operate. The result is a compute-only estimate unless the hourly cost already includes the rest of the system. It is not a universal GPU-versus-specialized-chip price comparison.
What the calculation tells you
The equation converts an hourly cost into a cost for a unit of delivered work. If a configuration costs H dollars per hour and sustains T tokens per second, it produces T × 3,600 tokens in an hour. Dividing H by that output gives a cost per token; multiplying by one million expresses the result per million tokens.
Use the same token definition on both sides of a comparison. If you count input and output tokens together, do so for both systems. If your business cares about generated output tokens, compare generated output tokens rather than mixing them with a total that includes prompt tokens.
When the simplified number is misleading
The result can understate cost if the hourly price covers only the accelerator while you separately pay for a host, storage, networking, electricity, cooling, deployment, or operations. It can also misrepresent a system that is paid for while idle if the throughput figure reflects only active serving periods. Conversely, do not apply an idle-time penalty twice if your measured throughput already averages over the billed period, including idle time.
Cost per million tokens is useful for comparing token-heavy serving workloads. It does not replace cost per request when requests differ substantially in size, nor does it show whether a configuration meets a latency service level. Report the metric alongside latency and quality.
Define an equivalent workload before comparing platforms
A cost comparison is meaningful only when the configurations do comparable work. A faster or cheaper result may simply reflect a smaller model, shorter outputs, lower quality, a looser latency target, or a different request mix. Write down the workload before choosing hardware or interpreting a benchmark.
Record the workload and service target
- Model and version: identify the exact model and any variant. A result for one model is not a general result for other models.
- Request mix: characterize input and output lengths, request types, and any meaningful variation in traffic. Use representative traffic rather than a convenient single prompt.
- Concurrency and demand: record the expected simultaneous requests and whether demand is steady, bursty, or seasonal.
- Quality requirement: state the accuracy or output-quality threshold that must be maintained. A configuration that falls below it is not a valid cost-saving alternative.
- Latency target: define the response-time service level the system must meet. A configuration that is inexpensive only when allowed to respond too slowly is not equivalent.
- Serving choices: record quantization, batch size, framework, and other serving settings. These can affect both throughput and quality.
Keep those conditions constant when benchmarking. If you change a setting to make a platform work well, document the change and verify the resulting quality and latency rather than treating it as a free performance gain.
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Check model support and engineering effort
Before treating a chip’s quoted or measured performance as attainable, confirm that the model and serving stack are supported on that platform. Establish what work is required to configure, optimize, validate, and maintain the deployment. The engineering effort may be worthwhile, but a lower accelerator bill does not by itself establish a lower total cost if adaptation and ongoing operations require additional resources.
That consideration matters especially when comparing a general-purpose GPU deployment with a purpose-built accelerator. AWS documents both GPU-based EC2 infrastructure and purpose-built Trainium and Inferentia infrastructure, but those options should be assessed against the selected workload rather than ranked by category alone.
Measure throughput at the latency and quality you need
Benchmark representative traffic on each candidate configuration. Record the throughput the system can sustain while meeting the workload’s quality and latency requirements. Do not substitute a vendor’s peak chip figure for measured useful throughput: peak specifications do not establish the performance of your model, software, request mix, or service target.
Capture the right performance data
- Useful throughput: tokens per second or requests per second delivered under the intended production conditions.
- Latency: the observed response time at the target concurrency and traffic mix. A throughput gain that misses the latency target does not qualify as equivalent performance.
- Quality: verify that output quality remains within the requirement for the configuration being measured.
- Utilization: record how much of the paid capacity is productively serving work over the measurement period.
- Power draw: capture the power used under the measured workload if energy is part of the operating-cost calculation.
For workloads where prompt processing and token generation behave differently, measure prefill and decode separately as well as overall results. This helps explain a result: two systems with similar total throughput may behave differently for a workload dominated by long inputs versus one dominated by long generated responses. Use the combined workload result for the final comparison if that reflects actual traffic.
Measure serving behavior, not just accelerator activity
Batching, concurrency, serving software, and demand patterns affect delivered throughput. A configuration may perform well when requests can be grouped efficiently but less well when traffic is sparse or latency-sensitive. Benchmarking only at a high, steady load can therefore overstate what a service will achieve during quiet periods or under a different request mix.
AWS recommends continually evaluating accuracy, latency, and cost while refining deployments to maximize utilization of underlying resources. That advice is useful across deployment choices: an initial benchmark is a baseline, not proof that the same unit economics will persist as demand or software changes.
Build a comparable cost for rented or owned systems
First make the cost boundary explicit. If you compare a cloud accelerator’s hourly rate with an owned system’s purchase price, or compare accelerator-only prices with full-system costs, the result is not like for like. Include costs that are necessary to deliver the measured service, and state which ones are already included in the hourly rate.
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For rented infrastructure
Start with the relevant hourly price for the region, instance or configuration, and pricing mode you would actually use. Then determine whether the rate includes the host and other resources required by the deployment. Account separately for any additional storage, networking, or idle minimums that affect the bill. Use current prices for the intended region and terms; no single rate is a valid stand-in for every provider, region, or purchasing arrangement.
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For owned infrastructure
Convert the purchase and deployment cost into an hourly equivalent over a stated useful life. Make the expected utilization assumption explicit: the same hardware purchase spread across more productive service hours results in a different unit cost than hardware that spends much of its life idle. Include the host, networking, energy, facility overhead, and operations required to keep the system serving reliably when those costs are not already represented elsewhere.
NVIDIA identifies capital expenditure, energy, memory bandwidth, and utilization as total-cost considerations. The expanded checklist here is a practical accounting method, not a claim that every deployment has the same cost structure. Use costs and assumptions from the environment you are evaluating.
Include energy and facility costs from the actual deployment
Use the deployment’s own power measurements and electricity assumptions. Add facility overhead such as cooling where it applies and is not already included. Do not transfer an electricity rate or power-overhead assumption from an unrelated region or data center: geography and facility design can change the result.
Power draw is not itself an energy bill. To estimate energy cost, relate measured power to the hours the system operates and apply the relevant electricity price. Keep the time period and utilization assumptions consistent with the throughput and hardware-cost calculations.
Account for utilization without counting it twice
Utilization is the share of paid capacity that produces useful work for the workload being priced. A system that must remain available while demand is low can incur cost during hours in which it delivers few or no tokens. As productive utilization falls, the cost per delivered token rises if the system’s hourly cost remains unchanged.
There are two valid ways to handle this, depending on how you measured throughput:
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- Throughput measured over the full billed period: use the average tokens per second for that period, including idle time, with the hourly cost. The idle capacity is already reflected in the lower average throughput; do not apply a second utilization discount.
- Throughput measured only while actively serving: adjust the output used in the calculation to reflect productive time over the billed period, or calculate an all-in cost per hour of actual delivered service. State the utilization assumption used.
Demand peaks complicate the calculation. A system sized to meet peak traffic may be underused for much of the day, while a system sized for average demand may miss the latency target during bursts. Evaluate the capacity and utilization pattern that can meet the actual service requirement, rather than dividing the bill by a theoretical full-capacity output.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Calculate cost per token and cost per request
Once you have a comparable hourly cost and measured output, calculate both token and request economics where each matters to your service. For the token calculation, use the simplified NVIDIA form when H is the applicable hourly cost and T is measured tokens per second:
Cost per million tokens = H ÷ (T × 3,600) × 1,000,000.
If H includes only the accelerator, label the result as compute-only. If H is an all-in hourly equivalent that includes the costs in your chosen boundary, label it accordingly. The label prevents a partial estimate from being mistaken for the cost of running the complete service.
Use requests per second for request-based economics
If the relevant unit is a request, let R be measured requests per second under the same request mix and service target. An analogous calculation is:
Cost per million requests = hourly cost ÷ (R × 3,600) × 1,000,000.
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Cost per request is most useful when the request mix is stable or when you calculate it separately for meaningful request classes. If one platform is tested with shorter prompts or outputs, its apparent per-request savings may not carry over to the traffic you actually serve. Include tokens per request or request-class details alongside the metric so its scope is clear.
Find the break-even point instead of guessing a winner
For two systems measured on the same workload, the one with the lower ratio of hourly cost to useful throughput has the lower compute cost per unit of output. If A and B use comparable cost boundaries, A is cheaper per token when:
HA ÷ TA < HB ÷ TB.
Equivalently, A’s hourly cost can be higher and still break even if its throughput advantage is large enough. This relationship is a decision rule, not a claim about the performance or price of any specific GPU or specialized chip. Apply it only after confirming that both throughput figures meet the same quality and latency requirements and that both hourly costs cover comparable resources.
For a full operating comparison, use the all-in hourly equivalent rather than the accelerator rate alone. If the additional cost of engineering, infrastructure, or operations changes the cost boundary, include it before deciding which option is less expensive.
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A single estimate hides assumptions that may change the ranking. Recalculate the comparison under plausible alternatives for:
- Utilization and demand: include quiet periods and demand peaks that affect how much paid capacity delivers useful work.
- Electricity price: use the rates relevant to the actual location rather than a generic assumption.
- Equipment life: for owned systems, vary the period over which purchase and deployment costs are amortized.
- Throughput: assess whether observed performance is likely to hold for the representative request mix and required latency.
Identify which assumption changes the result most. If a small change in utilization or equipment life reverses the ranking, present the decision as dependent on that assumption rather than as a stable platform advantage. The calculation is only as transferable as its workload, price, and operating inputs.
A step-by-step comparison workflow
- Specify the workload. Record the model and version, input and output sizes, request mix, concurrency, quality requirement, and latency service level.
- Choose a serving configuration on each platform. Document quantization, batching, framework, and other relevant settings. Confirm model support and identify the engineering required to reach production quality.
- Benchmark representative traffic. Measure useful throughput at the target latency and quality. Record utilization and power draw; measure prefill and decode separately if they matter to the workload.
- Establish comparable hourly costs. For rented systems, use the relevant hourly price and account for storage, networking, and idle minimums. For owned equipment, amortize purchase and deployment costs over a stated useful life and expected utilization.
- Add deployment-specific operating costs. Include energy and facility overhead using the deployment’s actual electricity and cooling assumptions. Add host, networking, and operations where they are not already included.
- Calculate unit costs. Compute cost per million tokens and, where useful, cost per request for the same request mix. State whether each figure is compute-only or all-in.
- Report service performance alongside cost. Show latency and quality with the unit-cost result so a cheaper but slower or lower-quality configuration is not presented as an equivalent replacement.
- Test the assumptions. Recalculate for changes in utilization, demand peaks, electricity price, and equipment life. Explain which assumptions affect the ranking.
This sequence synthesizes the relevant cost, throughput, energy, utilization, software, and latency considerations. It is a practical comparison method, not an official standard published by a provider.
What current evidence can—and cannot—establish
NVIDIA’s June 17, 2026 TCO guidance supplies the simplified cost-per-million-token formula and includes vendor-hosted platform and benchmark claims. Any such benchmark applies to its named model, software, benchmark, and test conditions; it should be attributed as a vendor-reported result rather than treated as a neutral guarantee or a general GPU-versus-specialized-chip comparison.
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Accordingly, the defensible answer is a calculation tied to a defined deployment—not a universal claim that GPUs, TPUs, Inferentia, or another specialized accelerator always cost less. The platform that wins for one model and service target may not win for another.
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