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
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