Memory requirements

Each prepared model stays resident in a process of its own. The values are the physical footprint from vmmap --summary, measured 30 seconds after launch on an M5 (32GB).

Process	Model	Memory	Runs when	Measured on
Speech recognition	Qwen3-ASR 1.7B 8bit (MLX)	2.4GB	At launch (when speech recognition uses MLX)	2026-09-20
Searching the memory by meaning	multilingual-e5 small (ONNX int8)	655MB	At launch (when semantic search is on)	2026-09-22
Choosing the kind of backchannel	ModernBERT-ja 70m (ONNX int8)	325MB	At launch (in Japanese, with backchannels on, and not with a Live engine)	2026-09-20
Detecting the end of a turn	MaAI (PyTorch)	685MB	When the microphone is turned on (in Japanese, with MaAI on)	2026-09-20

With all four running, they use about 4.1GB. Choosing Qwen3-TTS for reading aloud uses about 2GB more. The app itself and its window each use about 90MB.