Artificial Intelligence is rapidly transforming the world. While cloud-based AI solutions dominate the industry, running AI locally offers greater privacy, control, and cost savings. Whether you’re developing AI-powered applications, fine-tuning large language models, or deploying AI agents for specific tasks, a robust local AI stack can be an excellent alternative to cloud solutions.

This guide explores how to set up and run a fully local AI solution using the following key components:

  • Ollama for running LLMs (Large Language Models)
  • Qdrant for Retrieval-Augmented Generation (RAG)
  • PostgreSQL as the SQL database
  • n8n for no-code workflow automation

Why Run AI Locally?

  1. Privacy & Security – No data leaves your machine, making it ideal for sensitive applications.
  2. Cost Efficiency – No recurring cloud compute costs.
  3. Customization & Control – Tailor the AI stack to your specific needs.
  4. Offline Capabilities – AI continues to function without internet access.

Setting Up Your Local AI Stack

1. Installing Docker and Dependencies

Since most AI components run in containers, ensure you have Docker installed. Download and install Docker Desktop for your OS.

2. Deploying the AI Components

Ollama – Running Large Language Models Locally

Ollama is a powerful framework for running open-source LLMs on consumer hardware. To install it:

curl -fsSL https://ollama.ai/install.sh | sh

ollama pull llama3

You can now run an AI model locally:

ollama run llama3 “What is the capital of France?”

Qdrant – Vector Search for RAG

Qdrant is a high-performance vector database essential for RAG-based AI.

docker run -d –name qdrant -p 6333:6333 qdrant/qdrant

Use the API to add and retrieve vector embeddings.

PostgreSQL – Relational Database for AI Data

PostgreSQL can store structured AI metadata.

docker run –name postgres -e POSTGRES_USER=admin -e POSTGRES_PASSWORD=admin -p 5432:5432 -d postgres

n8n – Automating AI Workflows

n8n is a no-code workflow automation tool that can orchestrate AI tasks.

docker run -d –name n8n -p 5678:5678 n8nio/n8n

Building a Local RAG AI Agent

A Retrieval-Augmented Generation (RAG) AI agent combines LLM capabilities with external knowledge from Qdrant and PostgreSQL. Here’s how you can create one:

  1. Store documents as vector embeddings in Qdrant
  2. Query Qdrant for relevant documents based on user input
  3. Combine retrieved data with LLM’s response
  4. Automate workflows using n8n

Hardware Considerations

  • A GPU with 8GB VRAM is recommended for running an 8B parameter model like LLaMA 3.
  • An i7/i9 or Ryzen equivalent CPU with multiple cores improves inference speed.
  • Use NVMe SSDs for faster model loading.

Performance & Optimization Tips

  • Use quantized models to reduce memory usage.
  • Adjust batch sizes for optimized inference speed.
  • Consider running on a dedicated GPU server for better performance.

Final Thoughts

Running AI locally is a game-changer, offering full control over your AI workflows. Whether using n8n for no-code automation or coding custom AI agents, this local AI stack provides a powerful, self-contained alternative to cloud-based solutions.

Are you ready to build your AI agent? Get started today with Ollama, Qdrant, PostgreSQL, and n8n!