Local LLMs are useful now, and they aren't just toys

local-llmprivacyvoice-assistanthome-assistantself-hosted

Abstraction: Practical real-world use cases where local LLMs beat cloud alternatives

Key points:

Best local LLM use cases: raw data processing (PDF search, unstructured-to-table conversion, Obsidian note tagging), proofreading (custom Chrome extension replacing Grammarly), and document management with PaperlessNGX OCR
Local voice assistant built with Whisper pipeline + local LLM + Home Assistant matches cloud speed for home control with zero privacy trade-offs; supports vague natural-language requests for music and weather
Models cited as capable for local data tasks: gpt-oss-20b, gemma-27b, seed-oss-36b with large context windows
Local LLMs do NOT compete with frontier models on complex multi-step reasoning, nuanced creative writing, or broad world knowledge — cloud still wins there
Key local advantages: no query logging, no cloud training on personal data, offline resilience, model version stability, and SearXNG-powered private web search
Hardware requirements have become reasonable; LM Studio and Ollama make setup accessible without a server rack

Connections: Home Assistant · Local LLM · Retrieval Augmented Generation