How to Set Up a Private LLM On-Premise

No data leaves your servers - the private LLM deployment for regulated and privacy-sensitive workloads.

Sending data to OpenAI or Anthropic is a non-starter for many regulated industries: healthcare finance legal government. Private LLM deployment on your own infrastructure eliminates the data privacy concern and can significantly reduce cost at scale. This guide covers the model selection serving infrastructure and API compatibility layer that makes private LLMs production-ready.

No fluff. Production-grade answers from engineers who ship AI into real products.

The Model Selection for Private Deployment

The private LLM landscape in 2025: Llama 3.1 70B is the performance baseline - it rivals GPT-4o-mini on most tasks and is free to self-host. Llama 3.3 70B instruction-tuned is the default choice for most private deployments. Mistral 7B and Mixtral 8x7B are strong options for European organizations with EU data residency requirements. Qwen 2.5 72B is competitive on multilingual and code tasks. For organizations with NVIDIA GPU infrastructure: NemoClaw (Vallettas security-hardened framework) adds OpenShell guardrails and audit logging to any open-source model.

At Valletta Software, we focus on:

Hardware: NVIDIA A100 80GB or H100 for 70B models - minimum 2x A100 for production 70B serving

vLLM: production LLM serving framework - OpenAI-compatible API continuous batching paged attention

Ollama: for development and small teams - single command deployment not for production load

Quantization: GPTQ or AWQ 4-bit quantization - run 70B model on 2x A40 instead of 4x A100

OpenAI-compatible API: vLLM exposes /v1/chat/completions - drop-in replacement for OpenAI SDK

Model storage: HuggingFace Hub download or air-gapped transfer - weights stored on local NFS

Load balancing: multiple vLLM instances behind nginx or Kubernetes ingress - horizontal scaling

The Security and Compliance Configuration

A private LLM deployment without proper security provides false confidence.

We give you more than just people. We give you top performers who drive results.

Network isolation: LLM serving inside VPC no public endpoint - only internal services can call it

Authentication: API key or mTLS authentication - not open HTTP endpoint

Audit logging: log every request and response with user identity timestamp - compliance requirement

Data encryption: encrypt model weights at rest encrypt requests in transit - TLS minimum

Access control: RBAC on who can call which model - not all users need access to all models

Guardrails: NeMo Guardrails or custom filters for output safety - especially for customer-facing applications

Backup: model weights backup to air-gapped storage - recovery from infrastructure failure

Your team, your direction. We handle the rest. Rates from EUR 45/h.

EU-incorporated in Malta - NDA on day one, full GDPR compliance. Trusted by startups and enterprise teams across 12+ industries.

Latest AI-Insights

Build RAG pipelines, agents, and LLM integrations from day one

Ship AI features 3x faster with AI-native tooling and methodology

Deploy to production - not just Jupyter notebooks and prototypes

Evaluate output quality - hallucination detection, cost optimization, monitoring

How to Set Up a Private LLM On-Premise - With Engineers Who Have Done EU-Regulated Deployments

Forget the hype. We make AI work in the real world.

Our engineers are trained in the latest AI tooling - Copilot, Claude Code, Cursor, LangChain, and vector databases - and use them daily to ship production AI features, not just prototypes.

Choose from a solo dev, mini team, or full squad. All powered by AI and ready to build from day one.

Lets keep it simple.

Our AI engineers deploy NemoClaw (our security-hardened private LLM stack) on your infrastructure: vLLM serving with OpenAI-compatible API network isolation RBAC audit logging and NeMo Guardrails - the setup that meets EU GDPR and HIPAA requirements.

How we work