Deploy Private AI on
Your Own Infrastructure

Take control of your data and reduce latency by deploying open-source Large Language Models (LLMs) like LLaMA 3 and Mistral on rented VPS environments. Build robust Retrieval-Augmented Generation (RAG) pipelines using high-performance vector databases.

Hardware Requirements

Choosing the right VPS hardware is critical for reasonable generation speeds. Models are heavy on RAM and memory bandwidth. Here is what you need depending on your target model:

VPS Deployment

Deploying a robust AI inference node requires the right tooling. We recommend Ollama for managing weights, quantization, and executing models efficiently via a REST API on your server architecture.

Installing the Inference Engine

# Install Ollama on Linux/Ubuntu VPS
curl -fsSL https://ollama.com/install.sh | sh

# Start the background service to keep API alive
systemctl start ollama
systemctl enable ollama

Running LLaMA & Mistral

Once the engine is running, pull open-source models provided by Meta or Mistral AI. The system handles quantization and hardware offloading automatically.

# Pull and run Mistral 7B
ollama run mistral

# Pull Meta's LLaMA 3 (8B parameters)
ollama run llama3

API Endpoints

REST API Generation Request

The local inference server exposes an API gateway on port 11434. You can interact with it via standard JSON payloads, making it easy to plug into web apps.

curl -X POST http://localhost:11434/api/generate -d '{
  "model": "mistral",
  "prompt": "Explain the core concept of Vector Databases.",
  "stream": false
}'

Vector DBs (RAG)

To build a Retrieval-Augmented Generation (RAG) system, you need to store document embeddings. We utilize ChromaDB running as a separate dockerized service. Python clients then bridge the Vector DB semantic search with the local LLM generation.

import chromadb
import requests

# 1. Connect to local ChromaDB instance
client = chromadb.HttpClient(host='localhost', port=8000)
collection = client.get_collection(name="company_docs")

# 2. Perform semantic search (retrieve context)
results = collection.query(
    query_texts=["How do I reset my password?"],
    n_results=1
)
context = results['documents'][0][0]

# 3. Pass Context to Local LLM API
payload = {
    "model": "mistral",
    "prompt": f"Context: {context}\n\nQuestion: How to reset password?",
    "stream": False
}
response = requests.post("http://localhost:11434/api/generate", json=payload)

Interact with the simulated local model below. This demonstrates how a self-hosted inference node processes background requests and returns typed JSON responses to the frontend client. Try asking about "Hardware", "RAG", or "Deploy".