Smaller models can now run on laptops, workstations, phones, and private servers. Local deployment can reduce data movement, improve offline availability, and provide predictable control, but it is not automatically private or inexpensive.
Good local use cases
Consider local models for classification, extraction, autocomplete, document routing, or workflows involving regulated data. A focused small model can be more efficient than a frontier model when the task is narrow and evaluation is clear.
Count the full cost
Hardware, power, updates, observability, security patches, and staff time belong in the calculation. Cloud APIs may be cheaper for irregular demand, while local inference can become attractive for steady volume or strict residency requirements.
Privacy needs architecture
A local model does not help if prompts are copied into logs, backups expose source files, or plugins send data elsewhere. Map the entire data path and apply retention, access, and deletion controls at every step.