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The reliable way to estimate model-serving cost is to divide the complete hourly cost of the deployment by the useful work it delivers at your target load. That means measuring output tokens, requests, latency and utilization—not multiplying a posted GPU rate by an assumed number of hours.
Start with the model and service requirements, verify that the accelerator configuration fits, collect an all-in hourly price, benchmark realistic traffic, then calculate cost per request and per million input or output tokens. The same H100 can produce radically different unit costs when concurrency and offered load change, so a single-user benchmark or GPU-only price is not enough.
1. Define exactly what you are costing
“The cost of running a model” can mean several different things. State the service boundary before collecting prices. An inference estimate normally includes the host that serves the model and the capacity held for reliability; a training estimate also needs the complete job duration, checkpointing and restart behavior. The calculations below are aimed at inference and other continuously available serving workloads.
Record the workload inputs
- Model size: parameter count, architecture and any mixture-of-experts routing behavior.
- Precision or quantization: for example, FP16, BF16, FP8 or an integer format. This changes weight memory, kernel choice and throughput.
- Context length: maximum and typical prompt length, including the generated conversation history. Longer context increases KV-cache memory and often lowers throughput.
- Token mix: average input tokens and output tokens per request. Keep the two numbers separate because prefill and decode stress hardware differently.
- Traffic: average and peak requests per second, burst pattern and expected concurrency.
- Latency target: time to first token (TTFT), token-generation rate and a tail target such as p95 or p99.
- Availability target: whether one process can be restarted, or whether a second replica, zone or region must be ready.
Write these values in a worksheet before comparing providers. A price that looks low at one request per second may be expensive when your latency target requires more replicas, while a high-throughput configuration may be wasteful for intermittent traffic.
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Choose the accounting unit
Use cost per request when requests have similar lengths and cost per million output tokens when response lengths vary. For chat or retrieval workloads, also calculate input-token cost; a system with long prompts can have a very different economics from one with short prompts even when output speed is identical. Report the traffic assumptions beside every result so another person can reproduce the estimate.
2. Verify memory, interconnect and parallelism before looking at price
A cheap accelerator is irrelevant if the model cannot fit or if the required tensor and pipeline parallelism misses your latency target. Account for model weights, runtime buffers, KV cache at the chosen context length, batching headroom and the serving framework. Quantization can reduce weight memory, but it does not eliminate cache or activation requirements.
Match the machine to the model
Google’s documentation describes A2 machines with A100 GPUs for large-model and cost-optimised inference. Its Google Kubernetes Engine guidance maps smaller models to L4 and larger single-node inference to H100. These are placement examples, not a universal ranking: your measured throughput and latency decide whether the mapping works for your model.
Check the accelerator memory available to the process, the host RAM needed to load and stage weights, and the interconnect between GPUs. If one GPU cannot hold the model and cache, determine whether tensor, pipeline or expert parallelism is supported by your runtime. More GPUs can increase capacity, but communication overhead can reduce useful tokens per second. Include the GPU count in both the hourly numerator and the benchmark configuration.
Separate capacity from performance
Memory capacity answers “can it run?” Performance answers “how much useful work does it deliver at the required latency?” Two configurations with enough memory can have different prefill speed, decode speed, batching behavior and tail latency. Do not use a per-GPU list price as a substitute for a multi-GPU benchmark.
3. Build the complete hourly cost
Use the provider’s price for the whole machine or instance, not just an accelerator line item. Google identifies the accelerator, vCPUs, system memory, local or persistent storage, networking, region and purchasing model as pricing factors. Add any capacity reserved for failover and the cost of idle capacity separately.
| Cost component | What to include | Why it changes the estimate |
|---|---|---|
| Accelerators | GPU or other accelerator count and model | Parallel replicas and model placement determine the total device-hours. |
| Host compute | vCPUs and system memory | Tokenisation, request handling, data movement and orchestration consume host resources. |
| Storage | Local scratch plus persistent disks or object-storage transfers | Weights, caches, logs and checkpoints can require more than the boot volume. |
| Networking | Inter-zone, inter-region and internet egress where applicable | Traffic and distributed parallelism can create charges outside the instance rate. |
| Region and placement | Specific region, zone or local-zone price | The same instance can have different rates and availability by location. |
| Purchasing mode | On-demand, spot, committed use or capacity block | Discounts trade flexibility or interruption tolerance for a lower rate. |
| Reliability allowance | Standby replica, second zone or spare capacity | Availability requirements mean you may pay for capacity that is idle during normal operation. |
| Operations | Monitoring, orchestration, deployment and engineering time | These are real costs even when they are not on the GPU invoice. |
Define an hourly numerator such as:
all_in_hourly = (instance_hourly_price × active_instances) + storage + networking + orchestration + reliability_allowanceKeep one-time engineering and model-conversion work separate from recurring serving cost. If you amortise those costs, state the period and expected request or token volume used for the amortisation.
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4. Benchmark sustained output under production-like load
Run the exact model, precision, context distribution, serving stack and hardware you intend to buy. Warm the model first, then send representative prompts at the expected concurrency and offered request rate. Record both throughput and latency; a high average rate is not useful if p99 latency violates the service-level objective.
Metrics to capture
- Requests per second actually completed.
- Input-token throughput and output-token throughput.
- TTFT and inter-token latency, with p50, p95 and p99 values.
- GPU utilisation, memory utilisation and host CPU or RAM utilisation.
- Queue depth, rejected or timed-out requests and batch size over time.
- Behaviour during bursts, replica starts, model reloads and failures.
Benchmark several offered-load points rather than one maximum. At low load, the GPU may be mostly idle; at high load, queueing can make latency unacceptable. The useful operating point is the highest sustained load that meets your latency and availability targets, with headroom for bursts.
Why concurrency changes the answer
A 2026 concurrency-aware study measured effective costs from $0.21 to $15.25 per million output tokens on identical H100 hardware under different offered loads. In the tested enterprise-load range, underutilisation produced penalties of 2.5× to 24×. The result is a warning against copying a benchmark performed with a different request rate: the hardware did not change, but the denominator of useful tokens did.
When publishing or circulating an estimate, include the concurrency, offered load, prompt and completion distributions, batching policy and latency target next to the cost number. Without those conditions, “cost per token” is not portable.
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Once the hourly numerator and sustained measurements are available, use consistent units. Let H be all-in dollars per hour, R be completed requests per second, and T be output tokens per second.
cost_per_request = H / (R × 3,600)output_tokens_per_hour = T × 3,600
cost_per_million_output_tokens = H / (output_tokens_per_hour / 1,000,000)For input tokens, substitute measured input-token throughput for T. If you need a blended figure, report input and output costs separately first, then apply your observed input/output mix; do not silently treat an input token as equivalent to a decode token.
Illustrative arithmetic
Suppose a machine is quoted at $3.673385 per hour and your benchmark at the required latency completes 10 requests per second. The request cost is $3.673385 ÷ (10 × 3,600), or about $0.000102 per request. That arithmetic is only valid for the stated 10-request-per-second operating point; changing concurrency changes the denominator.
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If a benchmark reports 66 output tokens per second per user, as in NVIDIA’s H100 page example for GPT-OSS-120B with vLLM, one user produces 237,600 output tokens per hour (66 × 3,600). To turn that into a price, divide your matched hourly host cost by 0.2376 million tokens. The NVIDIA figure is a vendor-page benchmark citing SemiAnalysis InferenceX in an April 2026 context, so treat it as an example condition rather than a universal H100 rate.
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Account for utilisation and redundancy explicitly
If a replica is available but averages 70% useful utilisation, either benchmark at that operating point or divide the cost by 0.70 as an idle-capacity adjustment—never both. Add standby replicas, rolling-deployment headroom and regional failover to the numerator. A service that must keep two replicas online has a different cost per token from a batch job that can stop when no work is queued.
6. Published price examples and how to read them
The following figures are reference points from provider tables, not like-for-like performance comparisons. They are tied to the listed configuration, location, purchasing mode and access date.
| Listing | Accelerator configuration | Published rate | Qualification |
|---|---|---|---|
| Google Cloud a3-highgpu-8g | Eight NVIDIA H100 GPUs | $88.490000119 per hour | Google Cloud price table, accessed 2026; machine-level rate. |
| Google Cloud a2-highgpu-1g | One NVIDIA A100 GPU | $3.673385 per hour | Google Cloud price table, accessed 2026; machine-level rate. |
| AWS p5.48xlarge, US East (Atlanta) Local Zone | Eight H100 GPUs | $41.528 per hour per instance, or $5.191 per accelerator | AWS published rate, accessed 2026; location is the Atlanta Local Zone. |
| Google Cloud a3-highgpu-2g spot | Two H100 GPUs | $12.614730137 per hour | Google Cloud spot table, accessed 2026; spot capacity is interruptible. |
These numbers cannot be ranked by dividing dollars by the GPU count alone. The eight-GPU machines may provide the memory, interconnect or throughput needed by a model that cannot use one GPU. Conversely, a one-GPU machine can be cheaper for a small model if its measured throughput meets the target. Always replace the posted rate with an all-in rate for the same region and host specification before comparing.
7. Choose on-demand, spot or a commitment
Purchasing mode changes both price and operational risk.
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| Mode | Billing and risk | Use it when |
|---|---|---|
| On-demand | Standard pay-as-you-go, billed per second with no long-term commitment. | Traffic is uncertain, deployments are experimental, or interruption is unacceptable and flexibility matters. |
| Spot | Discounted capacity that can be reclaimed or interrupted by the provider. | The workload can checkpoint, retry and tolerate evacuation, such as queued batch inference or training. |
| Committed use or reserved capacity | Lower rates in exchange for a time or capacity commitment. | Baseline demand is predictable enough to keep the commitment utilised. |
| Capacity block | Capacity purchased for a defined period; AWS publishes accelerator-specific Capacity Blocks rates. | A known future window requires guaranteed accelerator availability. |
Google documents the on-demand, spot and commitment choices; AWS publishes accelerator-specific Capacity Blocks rates. Compare the expected bill, not the nominal discount: include the cost of retries, checkpoint storage, spare capacity and the probability that spot capacity is unavailable in your region.
A practical purchasing decision
- Measure the minimum steady capacity needed to meet your latency target.
- Put that baseline on the least risky mode your availability contract permits.
- Use interruptible capacity for burst or retryable work only after testing restart and checkpoint time.
- Commit only the portion of demand supported by historical usage or a contract; leave uncertain growth on flexible capacity.
- Recalculate after each model, context-length or traffic change.
8. Rent versus buy: compare total ownership, not sticker prices
Renting converts accelerator ownership into an hourly operating cost and gives access to different regions and generations. Buying can make sense for sustained, predictable utilisation, but the comparison must include the server chassis, networking, power, cooling, space, support, financing, depreciation, spares and staff time. A purchased GPU that sits idle still incurs capital and facility cost.
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Use a monthly break-even worksheet:
monthly_rental = all_in_hourly_rental × billable_hours + variable_networking
monthly_owned = (purchase_cost ÷ useful_life_months) + power + cooling + space + support + maintenance + financingThen compare the same delivered output under the same latency and availability target. If buying requires extra machines for failover or a lower utilisation because demand is seasonal, include those machines. If renting requires a premium for a scarce region, include that premium rather than comparing against a different location.
9. Compare two accelerators or providers on the same axes
Build a side-by-side sheet only after both configurations pass the memory and parallelism check. Record the following for each candidate:
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| Comparison axis | What to record |
|---|---|
| Memory capacity | Usable device memory with the selected precision, cache and batching headroom. |
| Interconnect and GPU count | Topology, bandwidth and the number of devices required by the serving strategy. |
| Sustained output throughput | Measured output tokens per second at the target concurrency and context distribution. |
| Time to first token and tail latency | TTFT plus p95 or p99 values under the same offered load. |
| All-in hourly price | Accelerator, host, storage, network, region and purchasing mode. |
| Cost per million tokens | Input and output costs calculated from the matched measurements. |
| Region and availability | Actual zones, quota lead time and ability to add or replace capacity. |
| Interruption risk | Spot eviction behavior, restart time and recovery automation. |
| Commitment flexibility | Minimum term, cancellation or modification rules and expected utilisation. |
A lower hourly rate wins only if it delivers the required useful tokens with the required latency and uptime. A higher-rate machine can have a lower cost per token when it sustains more work or needs fewer replicas.
10. A repeatable estimation procedure
- Write the service contract. Set model version, precision, context limits, input/output token distributions, requests per second, peak concurrency, TTFT and tail-latency targets, and availability.
- List feasible configurations. For each accelerator, document memory use, host requirements, interconnect and GPU count. Remove configurations that cannot fit or meet the parallelism requirement.
- Collect regional all-in prices. Capture the exact instance name, region, billing unit, storage, networking and purchasing mode. Save the access date because provider tables change.
- Deploy a production-like benchmark. Use representative prompts, warm-up, batching and the same runtime planned for production.
- Sweep offered load. Measure several concurrency levels and keep only operating points that satisfy the latency target with headroom.
- Calculate unit costs. Apply the request and token formulas to each operating point. Report input and output costs separately.
- Add reliability and operations. Include standby replicas, idle capacity, storage, network, orchestration, monitoring and engineering amortisation.
- Stress the assumptions. Recalculate for longer contexts, higher traffic, lower utilisation, a second replica and a spot interruption. Show a range when demand is uncertain.
- Review after deployment. Compare billed hours, GPU utilisation, tokens and tail latency with the estimate. Update the worksheet when traffic or the model changes.
11. Common estimation errors and fixes
Using a GPU-only price
Failure: multiplying a per-accelerator figure by hours and ignoring the host, disks and network.
Fix: price the complete instance in the exact region and add recurring platform and reliability costs.
Comparing machines that do not fit the same model
Failure: choosing the cheapest single-GPU option even though weights, cache or parallelism require multiple GPUs.
Fix: perform the fit and interconnect check first, then benchmark only feasible configurations.
Extrapolating a single-user benchmark
Failure: treating one-user tokens per second as production throughput.
Fix: sweep concurrency and offered load, capture queueing and tail latency, and use the sustained point that meets the service target.
Ignoring input tokens and context growth
Failure: reporting output-token cost for a workload dominated by long prompts or growing conversation history.
Fix: measure input and output throughput separately and model the actual context distribution.
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Forgetting idle and failover capacity
Failure: dividing by peak throughput while paying for a second replica or long periods with no requests.
Fix: benchmark the expected utilisation and place standby, burst and deployment headroom in the numerator.
Assuming spot savings are free
Failure: applying the discounted rate without accounting for eviction, retries, checkpointing or regional scarcity.
Fix: test interruption recovery and include its compute, storage and engineering cost.
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Failure: comparing a local-zone rate with a regional rate, or a current on-demand price with an older spot quote.
Fix: carry region, machine name, purchasing mode and access date beside every number.
12. How to present the estimate to decision-makers
Lead with a range tied to explicit conditions: for example, “At the measured target-load operating point, configuration A costs X per million output tokens and configuration B costs Y.” Put the all-in hourly numerator, token and request denominators, latency percentiles, replica count and purchasing assumptions directly below it.
Keep three views in the same document: the steady-state bill, the low-utilisation case and the burst or failover case. Explain which assumptions are measured and which are planning allowances. This makes it possible to update one variable—traffic, context length, region or commitment—without rebuilding the entire estimate.
NVIDIA states that the important inference-TCO metric is cost per token, meaning the price-performance actually delivered. That metric is useful only when “delivered” is defined by your model, traffic and latency target. A disciplined estimate therefore ends with a benchmark configuration and a formula, not a hardware price copied from a catalogue.
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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.
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