Windchill AI Assistant Sizing and Value Calculator
This calculator sizes the GPUs and index for a private AI assistant on Windchill and compares monthly value with monthly hardware cost. Defaults suit a mid-sized engineering group.
Your numbers
Your results
Planning estimate. Replace defaults with pilot measurements, especially throughput and minutes saved.
GPU estimate by questions per active user
Sweeps Questions per active user per day from 2 to 60, holding your other inputs fixed.
Pilot measurement plan
Optional: get a short plan for measuring throughput and minutes saved in a two-week pilot.
Optional. The tool and every export work without this. No spam.
How to use it
Enter named users and the share who use it daily, then questions per user per day. Use the tokens per question your pilot logs report. Set GPU cost to the lease or depreciation per GPU per month. The chart sweeps questions per active user to show where a second or third GPU appears.
Formulas and assumptions
Questions per day equal users times active share times questions per user. Peak concurrency assumes 25 percent of questions fall in the busiest hour and an answer takes about 20 seconds. GPUs are the larger of the model floor and peak concurrency times 25 tokens per second divided by per-GPU throughput.
- Index GB = documents x pages x 2 chunks x 6 KB
- Hours saved = questions x minutes saved / 60 x 21
- Payback = one-off cost / net monthly value
Reading the result
A flat GPU count across a range of usage means memory, not throughput, is your constraint. Plan redundancy separately: one GPU has no failover. If monthly value is below GPU cost, the case rests on quality or risk benefits, not time saved.
Common mistakes
Treating named users as daily users, leaving out re-indexing and embedding load, and forgetting that long retrieved context multiplies tokens per question.
Frequently Asked Questions
Why is there a model memory floor?
A model must fit in GPU memory with room for context. A 70B class model in 8 bit needs far more memory than one card offers, so the floor can exceed what throughput alone would require.
How large will the index be?
For hundreds of thousands of documents, usually tens of gigabytes. Embedding dimension, chunk size and metadata change this, so treat the output as an order of magnitude.
Does the estimate include redundancy?
No. Add a spare GPU or a second node if downtime is unacceptable, and include it in the GPU monthly cost.
Can I trust the payback figure?
Treat it as a conditional statement: if minutes saved hold, payback is about this long. Verify minutes saved with timed tasks in a pilot.
Netray can sanity-check these numbers against your Windchill data volumes and run a measured pilot. The calculator needs no contact.
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