01Separate the three layers first
Homebrew is a package manager for macOS and Linux, like a command-line software store. Ollama manages and runs local models. Qwen, DeepSeek-R1 or Gemma are the actual model files downloaded and executed. A chat interface is only a frontend for the local API. Each layer can be checked and replaced independently.
02Understand the four events in a first run
Install Ollama, select a small model suited to the machine, download its files on first run, load it into memory, then enter text for local inference returned token by token. Ollama’s local API defaults to `http://localhost:11434/api`. Check operating system and chip, available memory and disk, then start with a small quantized model. Download completion does not prove every interface, plugin, update or tool is offline.
03Treat size, memory and speed as tradeoffs
Labels such as 7B, 14B and 32B commonly describe parameter scale. Quantization stores weights with fewer bits and reduces disk plus memory, sometimes at a quality cost. Context and intermediate computation also require memory, so an 8GB file does not mean 8GB RAM will run smoothly. On a Mac, unified memory serves CPU and GPU; larger models, longer context and concurrent requests increase use. Follow the specific model page, logs and available memory.
04Local is more controllable, not automatically safe
On-device inference can reduce material leaving the device, but desktop interfaces, plugins, search tools, telemetry, synced folders and backups may still use the network. For sensitive material, inspect the full data path rather than checking only that the model is local. A local model is not a free cloud flagship: subscription cost becomes hardware, time, electricity and maintenance.
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