Cloud & On-Prem AI Solutions: Bringing AI In-House for Maximum Control

Introduction

Artificial Intelligence (AI) is transforming businesses, but the choice between cloud-based AI and on-prem AI is crucial. While cloud AI solutions from major providers like OpenAI and AWS offer convenience, they come with high costs, data privacy concerns, and reliance on third-party infrastructure.

With on-prem AI solutions, businesses can have full control over their AI models, data, and processing—running advanced AI capabilities entirely offline. For a fraction of the cost of long-term cloud subscriptions, companies can invest in powerful AI infrastructure tailored to their needs.

In this guide, we’ll explore:
✅ The hardware requirements for running AI locally
✅ Server-grade vs. SME-grade AI setups
✅ Costs and benefits of on-prem AI vs. cloud-based solutions
✅ How larger GPUs unlock more advanced models

1. Why Choose On-Prem AI Over Cloud AI?

Cloud-based AI solutions charge based on usage, and for businesses processing high volumes of data, the costs quickly add up. With an on-prem setup, businesses gain:

✅ Full control over data – No need to send sensitive data to third-party providers.
✅ Cost efficiency – A one-time investment avoids recurring cloud fees.
✅ No latency issues – Instant access to AI models without internet dependency.
✅ Scalability – Upgrade hardware as needed to support larger models.
✅ Better compliance – Keeps AI processing compliant with GDPR and other regulations.

2. Hardware Requirements: Building Your Own AI RAG System

A Powerful NVIDIA GPU is Essential

For AI training, fine-tuning, and RAG (Retrieval-Augmented Generation), you need a high-performance GPU. A RTX 30xx series or better (such as 3090, 4090, or A6000) is ideal for handling LLMs (Large Language Models) like LLaMA 3.2 and DeepSeek-R1.

Recommended Minimum Hardware (SME & Small Businesses)

💻 Entry-Level AI Workstation (£8,000+):
🔹 GPU: NVIDIA RTX 3090 / 4090 (24GB VRAM)
🔹 CPU: AMD Ryzen 9 / Intel i9
🔹 RAM: 64GB DDR5
🔹 Storage: 2TB NVMe SSD
🔹 OS: Ubuntu / Windows with WSL
🔹 Pre-installed Models: LLaMA 3.2, DeepSeek-R1

🏢 SME AI Server (£12,000+):
🔹 GPU: NVIDIA A6000 / RTX 6000 ADA (48GB VRAM)
🔹 CPU: AMD Threadripper / Intel Xeon
🔹 RAM: 128GB ECC RAM
🔹 Storage: 4TB NVMe SSD + 10TB HDD
🔹 OS: Ubuntu Server with Docker & CUDA
🔹 Pre-installed: Full AI stack with Ollama, Open-WebUI, RAG retrieval system

🏭 Enterprise AI Cluster (£25,000+):
🔹 GPU: 4x NVIDIA H100 / A100 (80GB VRAM each)
🔹 CPU: Dual Intel Xeon / AMD EPYC
🔹 RAM: 512GB+ ECC RAM
🔹 Storage: 8TB NVMe RAID
🔹 Networking: 100GbE Infiniband for distributed AI
🔹 Software: Advanced AI pipeline for model fine-tuning

3. AI Models & What They Can Do

Your AI system comes pre-installed with leading open-source models, capable of running fully offline:

✅ LLaMA 3.2 – Ideal for text generation, coding assistance, and document processing.
✅ DeepSeek-R1 – Advanced AI for complex research, analysis, and business automation.
✅ Mistral-7B / Mixtral – Optimized for multilingual support and reasoning.
✅ Whisper AI – High-quality speech-to-text transcription for businesses.

💡 Larger GPUs with more VRAM unlock bigger models! With A100, H100, or RTX 6000, you can run models like GPT-4-level AI without cloud dependency.

4. Cost Breakdown: One-Time Investment vs. Cloud AI Costs

💰 Cloud AI Subscription (Annual Costs)

• API Usage Fees (GPT-4, OpenAI, AWS Bedrock): £10,000 – £50,000+ per year.
• Enterprise AI License Fees: £20,000+ per year.
• Data Transfer Costs: Extra fees for large-scale processing.

💾 On-Prem AI Investment (One-Time Cost)

• SME AI Server: £8,000 – £15,000 (Ready to go, no recurring cloud costs).
• Enterprise AI Cluster: £25,000+ (Built for scaling, no cloud reliance).

📌 ROI of On-Prem AI: Most businesses recover costs within 12-18 months, compared to ongoing cloud expenses.

5. Optional Maintenance & Support Contracts

To ensure maximum uptime and performance, we offer:

🔧 Basic Support (£250/month) – Remote troubleshooting & software updates.
🔧 Enterprise Support (£750/month) – 24/7 monitoring, on-site assistance, and model optimization.
🔧 Full AI Lifecycle Management (£1,500/month) – Managed infrastructure, model training, and system scaling.

6. How to Get Started with Your Own AI System

✅ Step 1: Choose your hardware – Select from our SME or Enterprise AI solutions.
✅ Step 2: AI Pre-Installation – We set up LLaMA 3.2, DeepSeek-R1, and Open-WebUI.
✅ Step 3: Delivery & Deployment – System arrives ready-to-run, plug-and-play.
✅ Step 4: Optimize & Scale – Upgrade GPUs and memory for more AI power.

🚀 Want to run your own AI in-house? Contact us today to get started!