Choose a GPU and Edge device from the workload, not just TOPS
Short answer: Choose a GPU or Edge computer from measured workload, not only TOPS, core count or a datasheet price. Memory, thermals, power, I/O and operations often decide whether the system holds up.
What to capture
Record resolution, preprocessing, model/precision, stream count, concurrent requests, latency target, memory headroom, ambient temperature, duty cycle, power budget and fallback behaviour.
Sizing table
| Measurement | Evidence to keep |
|---|---|
| compute | model/runtime version and response time at concurrency |
| memory | peak usage, context/index and headroom |
| thermal/power | enclosure, ambient, duty cycle and throttling |
| operations | updates, backup, spares, telemetry and rollback |
Use Hardware as a starting inventory and put the test plan in Edge AI. If evidence is missing, label the choice proposed, not supported.
Limitations
The same device can behave differently with a new camera, driver, model or temperature. TOPS from different vendors is not a direct comparison.
Read next
What Edge AI is, and how it differs from IoT and Cloud AIWhy an AI POC works in a demo but fails in productionLocal AI or Cloud AI: an organisation decision tableContinue with the decision context
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