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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 & 11Estimate inference cost from the tokens your serving system can deliver while meeting its latency target—not from a chip’s advertised peak throughput. Divide the hourly cost of the capacity you are paying for by its sustained output tokens per hour, then express the result per million tokens. The answer is useful only when the workload, latency, utilization, billing unit and cost boundary are stated alongside it.
Define the workload and the cost boundary
Before comparing accelerators, decide what workload you are costing and which expenses belong in the calculation. Otherwise, two precise-looking cost-per-token figures may describe different services.
Keep the workload fixed
Record the model and version, input-to-output token mix, context length, request arrival pattern, concurrency, precision or quantization, serving software and deployment mode. Hold these constant when comparing systems. A change in any of them can change throughput, latency or both.
Choose what “cost” includes
An accelerator-only estimate divides accelerator charges by delivered tokens. A broader serving estimate may also include host CPUs and memory, storage, networking, power, cooling, staffing and availability costs. State which boundary you use and apply it consistently to every option. For owned equipment, include the purchase or lease cost spread over its useful life, plus relevant ongoing operating expenses; the resulting denominator is an effective hourly cost, not simply the original purchase price.
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- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
Measure throughput at the latency users require
Peak throughput is not necessarily usable throughput. A system pushed to saturation may serve more tokens while violating the response-time target. Google Cloud’s AI accelerator performance and benchmarking guidance recommends fixed-model comparisons, concurrency sweeps, and recording sustained throughput per chip at a latency-compliant operating point. For inference, Google summarizes the goal as maximizing throughput without violating latency requirements.
- Set the service target. Specify latency limits and the percentile that matters, such as P99. Track time to first token and time per output token when they are relevant to the service.
- Increase concurrency in a controlled sweep. Keep the workload and configuration fixed while increasing concurrent requests. Stop when the chosen latency SLA is violated.
- Record the usable operating point. Capture sustained output tokens per second for the complete serving system and per accelerator chip, along with the concurrency and latency at that point. Do not substitute peak or saturation throughput.
- Measure realistic demand as well as capacity. Run low, typical and peak expected request rates. If paid capacity is often idle, divide its cost by tokens actually served over the billing period—not by what a saturated benchmark could have served.
Use output tokens as the unit in the calculation below. Input length and the input/output mix still matter because they affect the measured serving performance; cost per output token is not interchangeable with a measure that charges or counts input tokens as well.
Calculate cost per million output tokens
Let C be the effective hourly cost of the capacity being measured, and T the sustained output rate in tokens per second at the selected operating point. Then:
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- Flexible Integration: The MX3 can be easily integrated into your existing systems via its M.2 M-key form factor and support for Linux operating systems.
- Energy Efficient: The MX3 is designed to provide high performance while minimizing power consumption.
- Comprehensive Software Development Kit (SDK): The MX3 is supported by a comprehensive SDK that simplifies development and deployment.
- Hardware compatability: The MX3 is compatible with the PCI-SIG M.2 M-key 2280 Specification. It can be used with the Raspberry Pi 5 with a M-key 2280 HAT.
Cost per million output tokens = C × 1,000,000 ÷ (T × 3,600)
This converts an hourly cost and a per-second output rate into a cost per million tokens. For example, if a hypothetical system costs $8 per hour and delivers 1,000 output tokens per second at the required latency, the estimate is $8 × 1,000,000 ÷ (1,000 × 3,600), or about $2.22 per million output tokens. The example is arithmetic, not a quoted accelerator price or benchmark result.
For a workload that does not continuously achieve the benchmark rate, use its average delivered output tokens per second across the paid period. If a cloud instance is billed for an hour but serves only half the tokens implied by the benchmark during that hour, using the benchmark rate would understate the effective cost per token.
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- ✅Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor. 2.5W typical power consumption
- ✅Scalable, enabling simultaneous processing of multi-streams & multi-models
- ✅Enabling real-time, low latency and high-efficiency AI inferencing on the edge devices
- ✅Supports TensorFlow, TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- ✅Supports Linux and Windows. Supports the temperature range of -40°C to 85°C
Match cloud prices to the capacity being billed
For rented infrastructure, use the actual product-specific and regional rate, along with its billing unit and any applicable commitment. Google Cloud’s TPU pricing documentation says rates vary by product, deployment model and region; TPU charges accrue while a node is in READY state, and listed prices are per chip-hour. A TPU VM can contain multiple chips, while console billing may be shown in VM-hours. Confirm that the rate and measured capacity use matching units before calculating cost.
The Google Cloud pricing page, accessed in 2026, listed these on-demand examples. They are regional product prices, not universal TPU rates:
| Accelerator | Region | Listed on-demand rate | Unit and qualification |
|---|---|---|---|
| Ironwood | us-central1 (Iowa) | $12.00 | Per chip-hour; Google Cloud pricing page accessed in 2026. Recheck the live regional rate before budgeting. |
| Trillium | us-east1 (South Carolina) | $2.70 | Per chip-hour; Google Cloud pricing page accessed in 2026. Recheck the live regional rate before budgeting. |
| TPU v5p | us-east5 (Columbus) | $4.20 | Per chip-hour; Google Cloud pricing page accessed in 2026. Recheck the live regional rate before budgeting. |
Do not multiply a per-chip rate by VM-hours, or divide a VM-hour charge by per-chip throughput, without first reconciling the number of chips and the billed quantity. Also verify current regional prices and the pricing terms you will actually use; displayed on-demand rates do not establish a committed-use or other deployment price.
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- 48GB AI graphics accelerator
Account for load, utilization and operating costs
Cost per token changes with the relationship between capacity paid for and capacity used. Underused infrastructure spreads its hourly cost across fewer delivered tokens; a high-load benchmark alone can therefore make a lightly used deployment appear cheaper than it is in practice.
A June 2026 arXiv preprint by Chitral Patil reports a range of $0.21 to $15.25 per million output tokens across tested conditions on identical H100 hardware. That range belongs to the paper’s specific model, serving and load conditions; it is not a general H100 cost estimate or a multiplier to apply to another deployment. Its relevance is that load conditions can materially change measured cost, so measure your own expected demand pattern.
For a broader TCO estimate, add applicable costs that are outside the accelerator-hour charge, such as host capacity, storage, networking, power, cooling, staffing and availability provisions. Include each item only once, define the time period, and use the same boundary for every option. If some costs are not known, show the accelerator-only estimate separately rather than silently treating it as full TCO.
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- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
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Use published benchmark figures as bounded examples
Vendor results can illustrate the method, but they are not a universal ranking. NVIDIA’s 2026 vendor-published H200 and GB300 NVL72 comparison gives $4.20 and $0.12 per million tokens, respectively, for that particular comparison. NVIDIA also cites SemiAnalysis InferenceX reporting $0.123 per million tokens for GB300 NVL72 at 116 tokens per second per user, as of April 2026. These figures are tied to named systems, workloads and benchmark conditions; they do not establish what another model, serving stack, latency target, region or cost boundary will cost.
When using any vendor or benchmark number, preserve the system configuration, model and workload, serving software, latency or interactivity condition, units and benchmark date. NVIDIA’s benchmarking material refers to MLPerf Inference and InferenceX; its selected comparisons should not be treated as independently established rankings for all inference workloads.
Build a like-for-like comparison
A useful comparison reports enough information for another reader—or your own finance and engineering teams—to reproduce the assumptions:
- Accelerator and full system configuration, deployment region and rented or owned status.
- Model and version, input/output token mix, context length, precision or quantization, and serving software.
- Latency target and measured percentile, concurrency, sustained output tokens per second, and per-chip throughput.
- Utilization or request-load assumptions, including how much paid capacity is idle.
- Hourly cost, exact billing unit, commitment or pricing basis, and whether the figure is accelerator-only or broader TCO.
- Calculated cost per million output tokens and the measurement date.
Include a representative dense model and, if your deployment uses them, a representative sparse/MoE or reasoning model. There is no universal winner established by a chip name or a single published cost figure: the relevant result is the system that meets your workload’s service target at an acceptable, consistently defined cost.
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