How do you deploy and manage offline AI models using Ollama?

Ollama provides a streamlined experience for running LLMs locally with a simple CLI and REST API. Installation: single binary download for macOS, Linux, and Windows. Usage: ollama pull llama3 downloads the model, ollama run llama3 starts interactive chat, and ollama serve exposes an OpenAI-compatible REST API on port 11434. Model management: Ollama maintains a local model registry, supports Modelfiles for custom configurations (system prompts, parameters, adapter layers), and handles GGUF model imports. Architecture: Ollama automatically detects available GPU (NVIDIA CUDA, AMD ROCm, Apple Metal) and configures optimal execution, falls back to CPU when GPU is unavailable, manages VRAM allocation across multiple models, and supports concurrent request handling. Advanced features: custom Modelfiles define model behavior (FROM basemodel, PARAMETER temperature 0.7, SYSTEM "You are a helpful assistant"), model creation from safetensors or GGUF files, embedding generation via the /api/embeddings endpoint, and vision model support for multimodal inputs. Production deployment: run behind a reverse proxy (nginx) for TLS and authentication, set OLLAMAHOST for network binding, configure OLLAMAMODELS for custom storage location, and use Docker for containerized deployment. Integration: OpenAI-compatible API means existing code works with minimal changes (change baseurl to localhost:11434), SDKs available for Python, JavaScript, and Go. Limitations: single-node only (no distributed inference), limited batch processing optimization, and model loading latency on first request.