Local AI

How to Deploy a Private LLM on Your Own Infrastructure

By Gilad Levi · Updated September 2026

Deploying a private large language model is no longer a research project. With open-weight models, compact inference engines, and affordable GPU hardware, a small team can run production-grade AI entirely on their own infrastructure. This guide walks through the decisions you will face.

1. Start with the model

You do not need the biggest model. Most business use cases — document Q&A, summarization, classification, and lightweight coding assistance — run well on models between 7B and 32B parameters. Good candidates include:

• Llama 3.1 / 3.2 by Meta (8B, 70B, 405B)
• Qwen 2.5 by Alibaba Cloud (7B to 72B)
• Mistral Large / Small by Mistral AI
• DeepSeek for reasoning-heavy tasks

The right model depends on quality expectations, latency tolerance, and the hardware budget. A quantized 7B model can run on a single consumer GPU; a 70B model needs enterprise-grade hardware.

2. Choose the inference engine

The inference engine loads the model weights and serves an API. Common choices are:

• vLLM — fast, production-ready, strong throughput. Best for multi-user deployments.
• llama.cpp / Ollama — easy to set up, great for single-machine and local development.
• TGI (Text Generation Inference) — Hugging Face's server, good for Hugging Face model workflows.
• TabbyAPI / Tabby — focused on local AI coding assistants.

3. Pick the hardware

At minimum, you need a GPU with enough VRAM to hold the model weights:

• 7B model, 4-bit quantized: ~6 GB VRAM
• 13B model, 4-bit quantized: ~10 GB VRAM
• 70B model, 4-bit quantized: ~40 GB VRAM

Consumer cards like the RTX 4090 work for small models. Larger deployments use NVIDIA A100, H100, or L40S GPUs. If no GPU is available, CPU inference is possible but much slower.

4. Add a vector database for RAG

A private LLM on its own does not know your documents. To make it useful, pair it with retrieval-augmented generation (RAG): chunk your documents, store embeddings, and retrieve relevant snippets before generating a response. Common tools:

• Qdrant — fast, open-source, easy to run locally.
• Chroma — developer-friendly, integrates well with LangChain.
• pgvector — if you already use PostgreSQL.

5. Wire in an orchestration layer

You need code that handles document ingestion, prompt construction, tool calling, and session memory. Options include:

• LangChain — large ecosystem, good for complex pipelines.
• LlamaIndex — strong for RAG and data connectors.
• CrewAI / AutoGen — for multi-agent workflows.

6. Set access controls and audit logging

Because all data stays inside your network, you must be deliberate about who can prompt the model and what happens to the logs. Plan for:

• Authentication (SSO, API keys, or internal identity provider)
• Role-based access to sensitive documents
• Audit logs for compliance and security review
• Retention policies for prompts and responses