Local private on-premise LLM.

How do you deploy a local on-premise LLM for a business?.

Start with model selection and hardware assessment — before the first inference

Deploying a local on-premise LLM involves several sequential steps: choosing the open-source model (Llama 3, Mistral, Qwen and equivalents) for your use case and hardware budget, preparing the server or private cloud, installing the runtime (Ollama, vLLM, llama.cpp), setting up an API gateway to integrate with corporate systems and fine-tuning access permissions. It is critical to verify that data genuinely does not leave the perimeter — this is enforced at the network-rules level.

1. 1Define the use case: document summarisation, knowledge-base search, chat assistant or text generation.
2. 2Assess hardware requirements: GPU/CPU, VRAM and RAM for the chosen model size (7B, 13B, 70B parameters).
3. 3Choose the open-source model: Llama 3, Mistral, Qwen or another suited to your task and data language.
4. 4Prepare the server or private cloud: isolated network, firewall, no external access.
5. 5Install the runtime: Ollama for a simple start or vLLM/llama.cpp for high throughput.
6. 6Set up a REST API or OpenAI-compatible gateway to integrate with corporate applications.
7. 7Check network rules: confirm the model has no outbound connections to external servers.
8. 8Run a pilot on real data, measure performance and accuracy — then scale.

How does a local LLM differ from cloud AI?.

The key difference is where the data goes and who controls the model

Cloud AI services (OpenAI, Google Gemini, Anthropic) process your data on their servers — fast and infrastructure-free, but data leaves the company perimeter. A local on-premise LLM runs on your own hardware: queries and documents stay inside, and you have full control over the model and its behaviour. Initial cost is higher, but it is cheaper at high query volumes because there are no per-token fees.

Which open-source models are suitable for corporate on-premise deployment?.

Llama and its equivalents lead open-source for private deployment

Among open-source language models for on-premise deployment, the leaders are Llama 3 (Meta AI) with open weights, Mistral and Mixtral (Mistral AI), Qwen (Alibaba Cloud) and Phi (Microsoft). The choice depends on the use case (summarisation, search, code generation), available VRAM and multilingual requirements. Llama 3 is the most proven choice for enterprise scenarios: large community, active tooling support and open weights for commercial use under Meta's licence terms.

Conclusion

In short: when you need a local LLM and what to expect.

Local on-premise LLMs are the solution for businesses that cannot send data to external services: lawyers, doctors, finance professionals, public sector. Open-source models like Llama are deployed on your own server or private cloud: data stays inside the perimeter, GDPR compliance is built-in and there are no per-token fees at high query volumes. A pilot takes 1–4 weeks depending on the use case and infrastructure. Cloud AI is faster to start, but a local LLM gives full control over the model and its behaviour. Request a quote — we will prepare your deployment plan and estimate for "AI & automation" within 24–48 hours.

Frequently asked questions.