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Installing Ollama on an Apple Silicon Mac is as simple as running one Homebrew command. Then pick a model based on available RAM (Phi-3 for 8 GB, Llama 3.1 8B for 16 GB) and expose the local, OpenAI-compatible HTTP API on port 11434 to plug it into your own applications.
Ollama makes it trivial to run models like Llama 2 or Mistral on your own computer: one binary, one command, and quantised weights downloading to disk with no compilation required. Covers installation on macOS, Linux, and Windows with an honest look at what local inference can and cannot do compared to frontier models.
Federated learning trains AI models collaboratively across many devices or organisations without moving the original data: each participant trains locally and sends only gradients to the central server. Formalised by Google in 2016, it does not guarantee privacy on its own: it needs differential privacy or secure aggregation to prevent leaks from those gradients.
