Siemens Opcenter Execution AI Assistant Sizing and Value Calculator
This calculator sizes a private, on-prem AI assistant for Siemens Opcenter Execution and estimates its value. Enter users, usage, document volume and costs to see peak concurrency, the number of GPUs, the index size, hours saved and a simple payback.
Your numbers
People with access to the assistant.
Share of named users who ask at least one question on a typical day.
Typical range is 2 to 10 once people trust the tool.
Includes retrieved context and tool results. Grounded answers are often 1,500 to 4,000 tokens.
SOPs, work instructions, logs, reports.
Be conservative. Measure it in the pilot.
Aggregate generated plus processed tokens per second for your model and GPU under batching. Measure on your hardware; this default is a planning figure.
Amortised hardware, power and support, or private cloud rental.
Your results
Planning estimate only. Throughput per GPU depends on model, quantisation, context length and serving software, so benchmark your own stack. Time saved is the weakest input, so measure it in a pilot before committing budget.
Monthly value and net value vs named users
Sweeps Named users from 20 to 1000, holding your other inputs fixed.
Check these numbers on a Opcenter pilot
Share your inputs and Netray can help design a short pilot that measures real usage, throughput and time saved. Optional.
Optional. The tool and every export work without this. No spam.
How to use it
Start with the people who will actually use the assistant, not everyone with a login. Set the daily active share and questions per user from a pilot or a similar rollout. Keep the tokens figure realistic: a grounded answer includes retrieved context and tool results, which is usually much larger than the visible question and answer. Then enter your documents, minutes saved per question and cost assumptions. The chart shows how value and net value change as the user count grows.
- Use conservative minutes saved
- Benchmark throughput per GPU on your hardware
- Add a spare GPU for availability
- Re-run after the pilot with measured data
The formulas and assumptions
Tokens per day equal active users times questions times tokens per question. The busy hour is assumed to carry 20 percent of daily questions, so peak tokens per second is daily tokens times 0.2 divided by 3600. GPUs equal that peak divided by the throughput you enter, rounded up. Peak concurrency divides peak requests by a 30 tokens per second stream rate. The index assumes two chunks per page at 1024 dimensions. Value assumes 21 working days a month.
Reading the result for Siemens Opcenter Execution
For Siemens Opcenter Execution, most of the cost is usually fixed: one or two GPUs, the integration work and the care of the data pipeline. That is why net value rises quickly with users up to a point. If net value is negative at your real user count, the honest conclusion may be that a smaller pilot, a shared model server, or your vendor's own tools are the better first step.
Common mistakes
People overestimate daily active share, ignore the retrieval context in token counts, assume vendor benchmark throughput without batching and long contexts, and count every saved minute as cash. Another frequent error is forgetting redundancy. Treat the output as a range, test it on a pilot, and update the inputs with measured numbers.
Frequently Asked Questions
Why is the GPU count so sensitive to tokens per question?
Because most tokens come from retrieved context and tool results, not from the question. Doubling the context roughly doubles the load, so trimming context with good retrieval and summaries is the cheapest way to cut GPUs.
What throughput per GPU should I enter?
Benchmark your chosen model and serving stack with realistic prompts under concurrent load. The default is a planning figure, and real results vary widely with model size, quantisation and context length.
Is the payback figure reliable?
It is a rough hardware-only measure under stated assumptions. It excludes implementation and data preparation, and the time-saved input is the least certain. Use it to compare scenarios, not to promise a return.
Does the index size include the model?
No. It covers only the vector index and metadata for your documents. Model weights need separate disk and GPU memory, typically tens of GB depending on the model and quantisation.
If you want these numbers checked against your actual environment and a pilot designed to measure them, Netray can size and build the private AI layer with your team.
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