# Local private on-premise LLM

https://seo7.es/en/servicios/inteligencia-artificial/ai-local-llm  
Updated: 2026-10-05

*AI & automation*

Language models on your server: full AI power without sending data to external services.

Open-source language models (Llama and equivalents) are deployed on your own infrastructure or private cloud. Data never leaves the company perimeter and GDPR compliance is built-in by design. Ideal for law firms, healthcare organisations, financial institutions and any business handling sensitive data.

- **Data stays inside** LLM queries and responses are processed on your own server: nothing is sent to external APIs.
- **GDPR & privacy** On-premise AI meets GDPR requirements and internal data-protection policies.
- **Open-source, no lock-in** Open models like Llama: no single-vendor dependency and no per-token subscription fees.
- **Built for sensitive sectors** Law, healthcare, finance, public sector: where data simply cannot leave the premises.

## Quick answer

Local on-premise LLMs are open-source language models (Llama and equivalents) deployed on a company's own server or private cloud. Data never leaves your perimeter: neither user queries nor confidential documents are sent to external services like OpenAI. This is the solution for businesses with GDPR obligations, law firms, healthcare, finance and government data. A pilot launches in two weeks. Request a quote and we reply within 24-48 hours.

Here we explain local private on-premise LLMs for businesses with strict confidentiality requirements. We cover the difference between cloud AI and local language models, which open-source models (Llama and equivalents) are suitable for enterprise deployment, what it costs and how it meets GDPR. By "local on-premise LLM" we mean open-source language models deployed on the client's own server or an isolated private cloud: data never leaves the company perimeter during either training or inference. This fundamentally differs from commercial APIs like OpenAI or Google Gemini, where data is sent to third-party servers for processing. Below: a comparison table of cloud AI vs local LLM, a step-by-step deployment plan, an expert quote and answers to common questions. At the end, our "AI & automation" packages and related services.

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

**Cloud AI vs local on-premise LLM**

| Parameter | Cloud AI (OpenAI and equivalents) | Local on-premise LLM |
| --- | --- | --- |
| Data privacy | Data is sent to the provider's servers | Data never leaves your server |
| Control over the model | Limited: provider updates the model without notice | Full: you fix the model version and behaviour |
| GDPR compliance | Requires DPA with provider, cross-border transfer risks | GDPR compliant by default: data stays within the perimeter |
| Cost at high volume | Scales proportionally with token count | Fixed infrastructure cost |
| Time to launch | Minutes: API key and done | 1-4 weeks including infrastructure setup |

## 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.

### Expert opinion

> Meta releases Llama models with open weights, allowing companies to run AI on their own infrastructure and keep data inside the perimeter without sending it to external services.
>
> Meta AI (Llama), Open Language Models Llama. Source: https://ai.meta.com/llama/

## 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 and we will prepare your deployment plan and estimate for "AI & automation" within 24-48 hours.

## Frequently asked questions

### What is a local on-premise LLM in simple terms?

It is a language model (AI similar to ChatGPT) that runs on your own server or private cloud. Your data (documents, queries, conversations) is not sent to external services like OpenAI. Everything is processed locally, inside your company perimeter. For the team it looks like a familiar chat, only inside the company.

### Which businesses need private LLMs the most?

Primarily law firms, healthcare organisations, financial institutions and public sector bodies. Also any business working with personal client data, trade secrets or data regulated by GDPR that cannot be shared with third parties. If you are unsure whether you need the private option, we start by checking which data goes to the cloud today.

### Does a local LLM comply with GDPR?

Yes. Since data does not leave your server, there is no cross-border transfer and no need to sign a Data Processing Agreement (DPA) with an external provider. This makes an on-premise LLM the safest option from a GDPR perspective. Log retention and access policies are described in the project documentation.

### What hardware do you need to run a local language model?

It depends on the model size. A 7B-parameter model runs on a server with a GPU and 16 GB VRAM or a powerful CPU with 32 GB RAM. For 13B you need a GPU with 24 GB VRAM. For 70B you need multiple GPUs or a dedicated server. We help you choose the right configuration for your use case and budget.

### Can a local LLM be trained on our company data?

Yes, there are several approaches: fine-tuning the model on your data (resource-intensive), RAG (Retrieval-Augmented Generation), which searches your knowledge base without retraining the model, and LoRA adapters for efficient fine-tuning. RAG is the fastest start for a corporate knowledge base.

### How does a local LLM compare to ChatGPT in quality?

Modern open-source models (Llama 3, Mistral and equivalents) are comparable to GPT-3.5 and approach GPT-4 on many tasks. For corporate scenarios with narrow specialisation and fine-tuning, they often outperform general cloud models. The main trade-off is compute resources on your side.

### How much does it cost to deploy a local on-premise LLM?

The cost comprises hardware (or private cloud rental) and setup work. A pilot deployment with one model takes 1-2 weeks to set up. Real prices for "AI & automation" are in the packages block on this page; request a quote for a cost estimate tailored to your scenario.

### What is Llama and why is it used for on-premise?

Llama is a family of language models from Meta AI with open weights. Open weights mean you can download the model and run it on your own server without depending on Meta. This is the key difference from GPT-4 or Gemini, which do not have open weights. Llama 3 is one of the most popular open-source models for corporate on-premise deployment.

### How do you integrate a local LLM with corporate systems?

Most modern runtimes (Ollama, vLLM) provide an OpenAI-compatible REST API. This means any application already working with ChatGPT can be switched to the local model by updating the endpoint address. Authentication, query logging and access controls are configured on top of that.

### In which languages do you run the on-premise AI project?

We run the project in Russian, Spanish and English, the studio's three working languages. Documentation, instructions and post-launch support are provided in your preferred language. The model itself answers in the language of the employee's query. Instructions for employees are written in the language they will use with the model every day.