Private AI Deployment on Your Own Infrastructure: A UK IT Manager's Guide

Why Deploy AI Privately?

As UK businesses move beyond AI experimentation into production deployment, the limitations of public AI services become critical. Data privacy, regulatory compliance, cost at scale, latency requirements, and the need for full audit trails all point toward private AI deployment for any serious operational use case.

Private AI deployment means running large language model inference on infrastructure you control — your own servers, a UK private cloud provider, or a dedicated hosted environment where you have full control over data flows. Your documents stay within your systems; no queries reach external APIs; no data is used for model training.

Hardware Requirements

LLM inference requires significant compute, particularly for larger models. The key resource is GPU VRAM (video memory), which must hold the model weights during inference.

• Small models (7B parameters, e.g. Llama 3.1 8B): 8–16GB GPU VRAM. Runs on a single NVIDIA RTX 3090 or 4090. Suitable for simple document tasks.
• Medium models (13–34B parameters): 24–48GB VRAM. Typically requires multiple consumer GPUs or a single professional GPU (NVIDIA A30, A100). Suitable for most business document processing.
• Large models (70B+ parameters): 80GB+ VRAM. Requires professional data centre GPUs (A100, H100). High capability but significant hardware cost.

For document processing tasks (not conversational AI), smaller quantised models (4-bit or 8-bit quantisation) deliver excellent quality at substantially reduced hardware requirements. A quantised 13B model can achieve near-parity with GPT-4 for structured document extraction tasks on a £2,000 consumer GPU.

Model Selection for UK Business Use Cases

The open-weight model landscape has matured rapidly. Top models for UK business document processing:

• Llama 3.1 (Meta): Excellent general-purpose performance; strong instruction following; available in 8B, 70B, and 405B sizes
• Mistral/Mixtral: Efficient architecture; strong performance relative to model size; good for resource-constrained deployments
• Gemma 2 (Google): Strong reasoning and instruction following; available in 9B and 27B sizes
• Command R+ (Cohere): Particularly strong for RAG use cases; good citation quality

Deployment Architecture

A typical private AI deployment for a UK SMB includes:

1. Inference server: Hardware with GPU(s) running a model serving framework (Ollama, vLLM, or LM Studio for simpler setups)
2. API layer: OpenAI-compatible API endpoint within your network, so existing tools work without modification
3. Application layer: The VP Lab-style interfaces your users interact with
4. Monitoring: Usage logging, error tracking, and performance monitoring
5. Access controls: Authentication and authorisation for AI access

Security Considerations

Private AI introduces new attack surfaces. Key security considerations for UK IT managers:

• Network isolation: the inference server should not be internet-accessible; all access via internal API
• Input validation: prevent prompt injection attacks via document content
• Output filtering: screen AI outputs for sensitive data before displaying to users
• Access logging: audit trail of all queries for GDPR compliance and security monitoring
• Model integrity: verify model weights against published checksums to prevent tampering

Implementation Timeline

For a typical UK SMB private AI deployment:

• Week 1: Use case definition, model selection, hardware specification
• Week 2: Hardware procurement/cloud environment setup, model deployment
• Week 3: Application interface deployment, integration testing
• Week 4: User training, pilot rollout, monitoring setup

VantagePoint Networks manages the full private AI deployment process for UK businesses. Contact us to discuss your requirements and receive a scoped proposal.