Private LLM Deployment

Open-weight models running on your own hardware or in your private cloud. Prompts, documents and outputs never leave your network.

Private LLM Deployment

Open-weight models running on your own hardware or in your private cloud. Prompts, documents and outputs never leave your network.

Why Run It Yourself

Sending data to a third-party model API isn't an option for every organization. When it isn't, the model has to come to the data.

Common reasons:

Regulated data such as health records or borrower files
Data residency laws or contractual restrictions
Predictable cost at high, steady volume
Full control over model versions and upgrades

Model Selection

The best open model for your use case depends on the task and on the hardware you have. We benchmark candidates on your own evaluation set before recommending one.

We check:

Accuracy on your tasks across Llama, Qwen, Mistral, Gemma and other open-weight families
Fit on your GPUs at the context length you need
License terms for commercial use

Deployment Stack

Production serving that exposes an OpenAI-compatible API, so your applications don't need to care which model is behind it.

Stack:

vLLM for production serving, Ollama for small deployments
Kubernetes or bare metal
Air-gapped installs with an offline model registry
No hardware yet? See Sovereign AI Infrastructure

bash

1vllm serve Qwen/Qwen2.5-32B-Instruct-AWQ \
2  --tensor-parallel-size 2 \
3  --max-model-len 32768 \
4  --api-key $INTERNAL_API_KEY

Private RAG & Agents

Everything we build for Enterprise AI and Agentic AI can also run fully inside your network.

Local components:

Local embedding and re-ranking models
Self-hosted vector stores: pgvector, Qdrant or Milvus
Access through your identity provider, with logs kept in-house

Private LLM Deployment

Why Run It Yourself

Model Selection

Deployment Stack

Private RAG & Agents

Related capabilities

Sovereign AI Infrastructure

AI Inference

Model Fine-Tuning