Infor M3 AI Assistant Sizing and Value Calculator
This calculator sizes the hardware and estimates the value of a private AI assistant running on your own servers in front of Infor M3. Enter user numbers, usage and your own assumptions to get GPU count, index size and payback.
Your numbers
Share of named users who ask at least one question per day.
Includes retrieved context. Document questions run higher than record lookups.
Assumption: depends on model size, quantisation and GPU. Measure on your hardware.
Amortised hardware, power and hosting.
Your results
Planning estimate only. Tokens per second, minutes saved and active share are assumptions you must replace with measured or surveyed numbers.
Monthly value vs GPU cost by named users
Sweeps Named users from 50 to 2000, holding your other inputs fixed.
Validate my Infor M3 sizing
Optional: share your numbers and get a short review of GPU count, index size and pilot plan.
Optional. The tool and every export work without this. No spam.
How to use it
Start with named users and the share who ask a question on a given day, then the number of questions and the average tokens for a prompt plus answer. Enter the documents you want searchable and the minutes a good answer saves. The outputs update live, and the chart sweeps named users.
- Use a pilot or survey to set the active share; 10 to 25 percent is common for early rollouts.
- Document questions use more tokens than record lookups, so raise the average if retrieval is heavy.
- Replace the tokens-per-second default with a measured figure for your model and GPU.
The formulas
Questions per day equals named users times active share times questions per user. Tokens per day is questions times tokens per question. Peak tokens per second is busiest-hour questions divided by 3,600 times tokens per question; GPUs are that figure divided by tokens per second per GPU, rounded up, plus spares. Index size is documents times pages times 3 chunks times 1,024 floats at 4 bytes with 50 percent overhead. Monthly value is questions times working days times minutes saved divided by 60, times loaded hourly cost.
How to read the result
If monthly value is well above GPU cost, payback is short and the sizing is not the constraint. If it is close, the assumptions on minutes saved and active share matter most; test them in a pilot before buying hardware. Index size is usually small compared with the model weights; the GPU count is driven by peak concurrency, not by user count alone.
Common mistakes and Infor M3 notes
Typical errors: using average load instead of the busiest hour, ignoring spares, and counting every user as active. For Infor M3 the data comes from your own system and documents, so no per-seat cloud fee applies; hardware, power, setup and people are the cost. Vendor tools may be enough if you only need standard reports.
- Assume nothing about throughput until you measure.
- Keep a spare GPU or a failover plan for working hours.
- Check licence terms of the open-weight model you choose.
Frequently Asked Questions
What tokens per second should I use?
The default is a planning figure for batched serving of a mid-size open-weight model on one modern GPU. Real throughput depends on model, quantisation, context length and software, so benchmark vLLM or Ollama on your hardware.
Why does peak matter more than daily volume?
GPUs must serve the busiest hour, not the daily average. If a quarter of questions land in one hour, capacity must cover that burst or users wait.
Is the value figure reliable?
It is only as good as minutes saved per question and the active share. Use a small pilot with timed tasks to set them, then treat the result as a range.
Does the index size include the model?
No. Index size covers vectors for your documents. Model weights add roughly several GB to tens of GB per model depending on size and quantisation.
If you want these numbers validated against your Infor M3 data volumes and a pilot, Netray can help you measure real usage and size the hardware.
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