Configure auto-configure Ollama when user needs local LLM deployment, free AI alternatives,
or wants to eliminate hosted API costs. Trigger phrases: "install ollama",
"local AI", "free LLM", "self-hosted AI", "replace OpenAI", "no API costs". Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
Auto-configure Ollama for local LLM deployment, eliminating hosted API costs and enabling offline AI inference. This skill handles system assessment, model selection based on available hardware (RAM, GPU), installation across macOS/Linux/Docker, and integration with Python, Node.js, and REST API clients.
Prerequisites
macOS 12+, Linux (Ubuntu 20.04+, Fedora 36+), or Docker runtime
Minimum 8 GB RAM for 7B parameter models; 16 GB for 13B models; 32 GB+ for 70B models
Optional: NVIDIA GPU with CUDA drivers for accelerated inference (nvidia-smi to verify)
Optional: Apple Silicon (M1/M2/M3) for Metal-accelerated inference on macOS
Disk space: 4-40 GB depending on model size (quantized weights)
Package manager: brew (macOS), curl (Linux), or docker (containerized)
Instructions
Detect the host operating system and available hardware using uname -s, free -h (Linux) or (macOS), and (if GPU present)
Network connectivity issue or Ollama registry unreachable
Check internet connection; retry with ollama pull --insecure behind corporate proxy
Out of memory during inference
Model size exceeds available RAM
Switch to a smaller quantized model (e.g., 7B instead of 13B); close memory-intensive applications
GPU not detected
CUDA drivers missing or incompatible version
Install CUDA toolkit >= 11.8; verify with nvidia-smi; restart Ollama service after driver install
Port 11434 already in use
Another service occupying the default Ollama port
Stop conflicting service; or set OLLAMA_HOST=0.0.0.0:11435 environment variable
See ${CLAUDE_SKILL_DIR}/references/errors.md for additional error scenarios.
Examples
Scenario 1: Developer Workstation Setup -- Install Ollama on a macOS M2 machine with 16 GB RAM. Pull codellama:13b for code generation tasks. Integrate with a Python FastAPI application using the ollama Python package. Expected throughput: 30-50 tokens/second on Apple Silicon.
Scenario 2: Air-Gapped Server Deployment -- Install Ollama on an offline Ubuntu server via pre-downloaded binary. Transfer model weights via USB. Configure as a systemd service with auto-restart. Serve llama3.2:7b via REST API for internal team use.
Scenario 3: Docker-Based CI Pipeline -- Run Ollama in a Docker container as part of a CI/CD pipeline for automated code review. Pull mistral:7b, expose the API on port 11434, and integrate with a Node.js test harness that sends code diffs for analysis.
