Local AI: What It Is and Why It Matters for Privacy

· Sarabi.Cloud

Local AI is an artificial intelligence model that runs on a computer or server you control, instead of on an outside provider's servers. The documents and questions you give it stay inside your perimeter: they do not travel to third-party services. It is a choice about where your data lives, most useful if you handle confidential information.

If you use ChatGPT, Gemini or Claude in a browser, you are using cloud AI: the model lives elsewhere and you send it your text over the internet. In this guide we look at how the local version works, why it matters for privacy, what GDPR and the AI Act say, what its limits are and when it is not worth it. You will also find a hypothetical example, the steps to get started and the most common questions.

How does local AI work?

A language model (LLM, Large Language Model) is a program trained on huge amounts of text, which can continue and rework a piece of writing. When you ask it a question, the computer running it performs an operation called inference: it calculates the answer word by word. In the cloud, that calculation happens in the provider's data centers. Locally, it happens on a machine of yours, which can sit in your office or be a server dedicated to you.

To run locally you need models you can download and run on your own hardware, usually open source (the model's code and weights are public). According to Hexagon Sistemi's page, the most used names include Llama, Mistral, Qwen and DeepSeek. Tools such as Ollama do not create or train models: they download them, start them and make them usable through an interface or a local server, as Data Masters explains.

A technique widely used in companies is called RAG (Retrieval-Augmented Generation). Before answering, the system searches your documents for the relevant passages and hands them to the model as working material. The answers then rely on your files and can point to the document they came from, with no need to retrain the model.

Why does local AI matter for privacy?

Whenever you type a name, an address, a health detail or a contract containing client data into an AI assistant, you are processing personal data. With a cloud service, that data goes to an outside provider. GDPR (the European data protection regulation) then requires you to know who processes what, on which legal basis and with which safeguards. The provider usually becomes a data processor, and a written agreement under Article 28 of GDPR is needed.

With a local model the data's path is shorter: it stays in your system. This reduces the number of parties involved, simplifies your record of processing activities and makes it easier to tell clients where their information ends up. That is why local AI appeals to law firms, accountants, clinics and small businesses with sensitive data.

Regulators' attention to the subject is real. On December 20, 2024, the Italian data protection authority (Garante) fined OpenAI 15 million euros, challenging among other things the legal basis for processing, the information given to users and age verification, according to lawyer Luisa Di Giacomo's summary on Diritto.it. The case concerns a large provider, yet it shows that questions about where data goes and on what justification are not theoretical.

One point to keep in mind: running locally reduces exposure of your data to outside providers, but on its own it does not guarantee security, answer quality or compliance, as Data Masters also points out. Access, backups and logs still need protecting. Risks such as prompt injection also remain (malicious instructions hidden in a document or web page, which the model reads and carries out).

Local AI and the AI Act: what changes?

The AI Act is the European regulation on artificial intelligence. Running a model in-house does not exempt you from its rules, because the obligations depend on how you use the system, not on where the server sits. After the simplification package known as the Digital Omnibus, the calendar is as follows, according to PMI.it and EffeDiligence:

If you want to dig into two pieces of the puzzle, we have already written about AI literacy and Article 4 and about Italy's AI Decree 160/2026 for small businesses. For training, see our page on AI Act training. This text is for information only and does not replace advice from a lawyer or your DPO (data protection officer).

What are the benefits of local AI beyond privacy?

According to Paolo Ceravolo's analysis on AgendaDigitale (January 27, 2026), there are four main benefits. The first concerns costs: after the initial hardware investment, mostly electricity and maintenance weigh in, instead of a bill that grows with every word processed (cloud providers charge per token, meaning per fragment of text). The second is stability: a downloaded model stays the same, while a cloud service can change without notice. The third is speed and the ability to work even without a connection. The fourth is independence from a single provider.

What are the limits of local AI?

The same analysis lists limits worth knowing before you start. Local models do not have real-time up-to-date information. The largest cloud models remain superior at very complex reasoning. The cloud copes better with unpredictable workload peaks. You also need someone with technical skills to install, update and monitor the system.

The solution many experts point to is a hybrid approach: local for repetitive and sensitive tasks, cloud for generic requests that do not touch confidential data.

What hardware does local AI need?

The key part is the graphics card (GPU), because it performs the model's calculations. Its memory, called VRAM, matters most. The bigger the model, the more VRAM it needs. A technique called quantization compresses the model so it fits in less memory, with a loss of precision that is acceptable for many tasks.

As an order of magnitude, AgendaDigitale mentions a workstation costing around 4,000 euros with a 24 GB VRAM GPU, able to run a mid-size quantized model. Larger models need several cards together. For a real production setup, sizing against actual use is what counts: how many people work with it, on which documents, with what response times.

A concrete example: when does it make sense?

Hypothetical example. A professional firm with eight people receives contracts, payslips and ID documents from clients every day. It wants an assistant that summarizes documents and answers questions like "which contract contains the withdrawal clause?". Sending everything to an outside service would mean assessing the provider, data transfers and written agreements for every tool. With a local system using RAG over its own archives, the documents never leave the firm and the answers cite the source file.

When is local AI not needed?

It happens more often than you might think, so it is worth saying plainly:

How do you get started with local AI?

  1. List the data you handle and which of it is personal or confidential.
  2. Pick a single use case with a measurable result, for example summarizing case files or searching your archives.
  3. Run a pilot on real documents, or very similar ones, with a few people.
  4. Check the quality of the answers by hand, comparing them with those of someone who knows the documents.
  5. Decide where the system will run: at your premises, on a dedicated server in a European data center, or in a private environment shared across several sites.
  6. Involve your DPO or legal advisor for the final compliance assessment.

What does Sarabi.Cloud do on local AI and privacy?

On the local AI and privacy page you can see how we work: local AI models and private LLMs for professional firms, clinics and small and medium businesses that handle sensitive data, with three installation options (at your premises, on a dedicated server or in a private cloud). We provide a private RAG over your documents, an Article 28 GDPR agreement attached to the quote and access logs. The fee is flat, with no per-token or per-use costs. The first step is a demo on documents similar to yours, even with the network disconnected. The final compliance assessment stays with your DPO or legal advisor. If your need is broader, also look at the page on automation and AI.

Frequently asked questions about local AI

Is local AI free? Open source models can be downloaded at no charge, but you need hardware, electricity, setup and maintenance. The real cost depends on the machine and how many people use it.

Do I need the internet to use it? To run the model, no: that is one of its advantages. You need a connection if you want updates or recent information, because the model alone does not have them.

Is it safer than ChatGPT? It reduces the number of parties who see your data. Overall security still depends on access, updates and controls around the model.

Can I use it without a developer? For trying it on your own computer, simple tools exist. For company use with several people and confidential documents, it is better to be supported by someone who sets up and maintains the system.

Does local AI put me in line with the AI Act? No, not automatically. The obligations depend on how you use it: transparency toward people who interact with the machine, literacy for those who use it and, in some cases, rules for high-risk systems.

Do you have confidential documents and want to use AI without sending them outside your company? Write to info@sarabi.cloud and tell us in two lines what data you handle, how many people would use it and where you are today. We will tell you plainly whether local AI suits you or whether something else is enough.

Sources: AgendaDigitale, Dalla nuvola alla scrivania; Data Masters, Guida a Ollama; Hexagon Sistemi, AI on-premise aziendale; PMI.it, AI Act dal 2 agosto 2026; EffeDiligence, AI Act e rinvio al 2 dicembre 2027; Diritto.it, Sanzione del Garante a ChatGPT.

Have a similar case or a doubt? Write to us at info@sarabi.cloud.