The Demo Worked on a 7B Model. Production Traffic Changed the Math.
The demo looked fine on a small model with one user. Then real traffic showed up, latency got ugly, and the original GPU choice stopped making sense. Why thi
Inference Pain | 6 min read | 2026-04-01
The demo looked fine on a small model with one user. Then real traffic showed up, latency got ugly, and the original GPU choice stopped making sense.
Why this happens
- demos are usually tested with tiny load and perfect patience
- production adds concurrency, queueing, and impatience
- a model that feels cheap at one request at a time can become painful under real usage
- people optimize for "it works" instead of "it responds fast enough"
What people get wrong
They think the model choice was wrong. Sometimes the model is fine. The real issue is that the compute plan was sized for a demo, not for production behavior.
Practical rule
| Situation | Better move |
|---|---|
| small model, light traffic, early testing | start with RTX 4090 |
| latency and concurrency are becoming the problem | move to A100 80GB |
| the workload is already clearly heavy and expensive | only then evaluate H100 |
The simple takeaway
If the demo worked and production did not, the lesson is not always "change the model." Sometimes the model is fine and the GPU plan is still stuck in demo mode.
Need a production-safe step up?
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