Use Case
The useful local-versus-cloud question is often not which model should do the entire job. It is which stages deserve frontier intelligence. Local AI can handle the repetitive intake work: transcribe recordings, OCR scans, remove duplicates, identify relevant passages, normalize formats, redact sensitive details, and assemble a compact evidence packet.
The frontier model then receives less noise and a clearer assignment. That can reduce uploaded private material, context consumption, token cost, and the chance that the important evidence is buried inside a giant undifferentiated prompt. The local layer should preserve links or references back to the original material so compression never becomes an invisible loss of evidence.
At 16 GB, this works well as a chain of focused tools for speech, OCR, extraction, redaction, and short summaries. The 32 GB class supports stronger synthesis across more documents and a more capable private retrieval layer. A 64 GB unified-memory system can keep larger collections and more of the preparation pipeline local, but difficult judgment still belongs with the frontier model or the human.
The safe boundary is simple: local AI prepares and proposes; it does not silently decide what evidence is disposable. Review the final packet for omissions before using it for consequential work, and retain the originals whenever the task matters.