Local language models are useful when data sensitivity, offline operation, or predictable per-request costs matter. They also place deployment and evaluation responsibilities on the operator.
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Start from the workload
Summarisation, extraction, coding assistance, and open-ended chat have different quality requirements. Test representative examples before choosing a parameter count or quantisation level.
Estimate memory honestly
Model weights are only part of memory use. Context, key-value caches, runtime buffers, and concurrent requests add overhead. Leave headroom for the operating system and application.
Operate it like a service
Record model and prompt versions, evaluate regressions, limit inputs, isolate sensitive data, and define what happens when the model fails. Local does not automatically mean secure or accurate.
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