Ollama Setup

Overview

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 vm_stat (macOS), and nvidia-smi (if GPU present)
Select appropriate models based on available RAM:
- 8 GB: llama3.2:7b (4 GB), mistral:7b (4 GB), phi3:14b (8 GB)
- 16 GB: codellama:13b (7 GB), mixtral:8x7b (26 GB quantized)
- 32 GB+: llama3.2:70b (40 GB), codellama:34b (20 GB)
Install Ollama using the platform-appropriate method:
- macOS: brew install ollama && brew services start ollama
- Linux: curl -fsSL https://ollama.com/install.sh | sh && sudo systemctl start ollama
- Docker: docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
Pull the recommended model: ollama pull llama3.2
Verify the installation by listing available models (ollama list) and running a test prompt (ollama run llama3.2 "Say hello")
Confirm the REST API is accessible: curl http://localhost:11434/api/tags
Configure integration with the target application using the appropriate client library (Python ollama, Node.js ollama, or raw HTTP)
Set up GPU acceleration if NVIDIA or Apple Silicon hardware is detected
Configure model persistence and cache directory if non-default storage location is required
Validate end-to-end inference latency and throughput for the selected model

See ${CLAUDE_SKILL_DIR}/references/skill-workflow.md for the detailed workflow with code snippets.

Output

Ollama installation confirmed and running as a system service or Docker container
Selected model(s) pulled and cached locally with verified inference capability
REST API endpoint accessible at http://localhost:11434
Integration code snippet for the target language (Python, Node.js, or cURL)
Hardware assessment report: OS, RAM, GPU availability, recommended models
Performance baseline: tokens per second for the selected model on local hardware

Error Handling

Error	Cause	Solution
`ollama: command not found`	Installation incomplete or PATH not updated	Re-run install script; restart shell session; verify `/usr/local/bin/ollama` exists
Model pull fails with timeout	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.

Resources

Ollama Official Documentation -- installation, model library, API reference
Ollama Model Library -- available models with size and capability details
Ollama Python Client -- Python SDK for local inference
Ollama JavaScript Client -- Node.js SDK for local inference
Hardware sizing guide: RAM requirements by model parameter count and quantization level

ollama-setup