A picker that maps local LLMs to hardware, hardware to LLMs
Useful lookup table, but spreadsheets and Reddit threads already solve this better.

Handy, pragmatic productizing of what used to be a messy forum hunt — guided questionnaire, quick spec inputs and a terminal paste auto-detect flow are thoughtful UX touches for non-experts. The searchable model database and explicit TTS/edge notes (Raspberry Pi-optimized Piper, Coqui XTTS, etc.) show they focused on real-world constraints. Still, it’s primarily curated guidance; without transparent per-model runtime benchmarks or reproducible test harnesses it reads more like an excellent cheat-sheet than a new technical solution.
Local LLM users, ML hobbyists, privacy-focused developers, makers building offline/edge AI setups
Useful lookup table, but spreadsheets and Reddit threads already solve this better.
Ranks models by actual benchmark scores instead of just fitting the biggest model in VRAM.
Curated directory of LLM guides when ollama and LM Studio already exist.
Bonsai 1-bit models make Pi 4 LLMs viable where Ollama usually chokes.
Local LLM + RAG for datasheets beats cloud AI for proprietary firmware.
One YAML config for three backends when Ollama already handles llama.cpp alone.