On-Premises RAG System

A reference architecture for deploying retrieval-augmented generation (RAG) systems entirely within your organization's infrastructure, ensuring complete data sovereignty.

Overview

This architecture enables organizations to deploy AI-powered document intelligence without sending any data to external services. All inference, embedding generation, and vector storage occurs on-premises, making it suitable for:

• Regulated industries (finance, healthcare, government)
• Air-gapped environments (defense, critical infrastructure)
• Data residency requirements (GDPR, data localization laws)

Architecture Diagram

┌─────────────────────────────────────────────────────────────────┐
│                    On-Premises Network                           │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────────────┐  │
│  │   Ollama     │  │   Qdrant     │  │    AnythingLLM      │  │
│  │  (Inference) │  │  (Vectors)   │  │   (UI + Pipeline)   │  │
│  └──────────────┘  └──────────────┘  └──────────────────────┘  │
│         ↑                ↑                      ↑               │
│         └────────────────┴──────────────────────┘               │
│                          │                                       │
│                    ┌─────┴─────┐                                │
│                    │   Users   │                                │
│                    └───────────┘                                │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Components

Component	Role	Recommended Version
Ollama	Local LLM inference	Latest stable
Qdrant	Vector database	1.x+
AnythingLLM	RAG pipeline + UI	Latest stable
Traefik (optional)	Reverse proxy	3.x

Security Boundaries

All data remains within your infrastructure:

• Documents never leave the network perimeter
• Embeddings generated locally via Ollama
• Vectors stored in on-premises Qdrant
• Queries processed entirely locally
• Audit logs retained on your infrastructure

Data Flow

1. Document Upload - Users upload via secure internal interface
2. Chunking - Documents split into semantic chunks (~500 tokens)
3. Embedding - Ollama generates embeddings locally (CPU or GPU)
4. Storage - Vectors stored in Qdrant with metadata
5. Query - User question embedded, similar chunks retrieved
6. Generation - Ollama generates response with retrieved context
7. Response - Answer returned with source citations

Deployment Options

Environment	Stack	Notes
Air-gapped	Ollama + Qdrant + AnythingLLM	No network access needed post-setup
Corporate VPC	Same stack + Traefik	TLS termination at reverse proxy
Hybrid	Cloud embeddings, local storage	Compromise: faster embeddings, local vectors

Hardware Requirements

Minimum (CPU Inference)

• RAM: 32GB
• CPU: 8 cores
• Storage: 500GB SSD
• Model: Llama 3 8B or Mistral 7B

Suitable for small workloads (<100 documents, light query load).

Recommended (GPU Inference)

• RAM: 64GB
• CPU: 16 cores
• GPU: NVIDIA RTX 4090 (24GB VRAM)
• Storage: 1TB NVMe
• Model: Llama 3 70B or Mixtral 8x7B

Supports larger document sets (1000+ documents) and concurrent users.

Enterprise (Multi-GPU)

• RAM: 128GB+
• CPU: 32+ cores
• GPU: 2-4x NVIDIA A100 (80GB each)
• Storage: 4TB+ NVMe RAID
• Model: Full-size models with quantization

Production workloads with thousands of users.

Cost Estimate

Item	One-Time	Monthly
Server hardware (recommended tier)	$10,000-15,000	-
NVIDIA RTX 4090	$2,000	-
Electricity (~400W continuous)	-	$50-100
Software licenses	$0	$0
Total	~$12,000-17,000	~$50-100

All software components are open source with no licensing fees.

Implementation Checklist

• Provision hardware meeting minimum requirements
• Install Ubuntu Server LTS or RHEL 9
• Deploy Ollama and pull required models
• Deploy Qdrant with persistent storage
• Deploy AnythingLLM with Ollama + Qdrant configured
• Configure TLS if exposing to internal network
• Set up backup procedures for vector database
• Document runbook for operations team
• Train users on interface and best practices

Next Steps

Ready to deploy a RAG system for your organization?

Book a Technical Scoping Call to discuss your specific requirements, hardware constraints, and implementation timeline.