Run tuned small language models directly on the edge hardware is the correct option because it delivers predictable, local inference that can meet strict latency targets and does not rely on network availability.

Executing a tuned small model on-device eliminates network round trips and the variability of remote endpoints, so responses are far more likely to arrive under 50 milliseconds in remote settings. Small, optimized models are also more likely to fit the memory, compute, and power constraints of rugged handhelds while still supporting practical language features.

Deploy compressed large language models locally on the edge devices is less suitable because compressed large models still often exceed the compute and memory of typical field handhelds and they can increase latency and power consumption compared with tuned small models.

Use a central small language model endpoint with asynchronous calls from devices remains dependent on network connectivity and it introduces round trip delays, so it cannot guarantee the low and consistent latency required at remote wind farms.

Integrate a centralized large language model endpoint with asynchronous communication is the poorest match because large centralized models increase bandwidth and latency demands and they require reliable connectivity that is not available in many remote field deployments.

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