Raspberry Pi 4 Tackles Local LLM

Technically plausible, but reliability is the hard part: the Pi can probably host a 1B-class model, yet the assistant will only feel alive if turn-taking stays fast. Ollama frames Llama 3.2 1B as an edge-friendly model, so this is less a memory problem than a CPU-throughput problem. The Pi 4's quad-core CPU has to share time across wake-word detection, audio I/O, TTS, UI animation, and inference, so latency compounds quickly. Sustained load also makes cooling matter, because a borderline setup can start throttling. If llama3.2:1b feels flaky or sluggish, 1B-class Ollama alternatives like gemma3:1b or phi3.5-mini are the obvious next tests. Tight prompts and compact memory/state handling will help, but if the goal is a snappy character, splitting orchestration from inference may be the better architecture.