Closed Loop with Ollama: AI on Documents Without External Data Transfer

A medical center wants to implement an AI assistant to answer patient questions based on protocols. A law firm – for searching client cases. A government agency – for working with official documents. All three have one common question: "Will our data stay with us?" With cloud services, the answer is always "not entirely." With a closed circuit – yes, technically and legally. Short answer: a closed circuit is an AI assistant where all components (model, database, documents) reside on your server. No query is sent to third-party AI providers.

📚 Contents

• What is a Closed Circuit in the Context of AI
• How Ollama Works and Why It's Suitable for Isolated Environments
• Available Local Models: Comparison and Industry Recommendations
• Multilingualism: How Local Models Handle Ukrainian, English, and German
• Architecture: Server + pgvector + Ollama without Data Transmission
• Local Model Performance vs. GPT-4o: Real Numbers
• Hardware and Server Costs: What a Closed Circuit Costs
• Who Needs a Closed Circuit as a Necessity, Not an Option
• Step-by-Step Deployment Plan
• Frequently Asked Questions
• Conclusions
• Want to Deploy a Closed Circuit?

What is a Closed Circuit in the Context of AI

To understand what a closed circuit is, let's compare it to how most businesses use AI today. The difference is significant and directly impacts your GDPR compliance.

Cloud AI: An employee asks a question → the question's text and a document fragment are sent to OpenAI or Notion servers in the US → the model processes it there → the answer is returned. During this process, your data has been on someone else's server, in the provider's logs, and potentially accessible to their technical staff. For companies with client agreements, medical data, or HR documentation, this isn't an abstract risk but a concrete legal problem.

Closed Circuit: An employee asks a question → the query is processed on your server → a local model generates the answer → the answer is returned. The entire AI process occurs within your server. Neither OpenAI, Google, nor any other provider has access to your query.

An important clarification: "closed circuit" doesn't mean the server is physically isolated from the internet. Your employees and clients connect to the system through standard channels – a Telegram bot, a web chat on your site. However, the AI processing exclusively happens on your server, without transmitting data to external services.

Three Levels of Isolation — Which to Choose for Your Business

Not everyone needs the maximum level of isolation. We at AskYourDocs select the level with the client depending on the industry, regulatory requirements, and budget:

Level 1: Hybrid Mode. Documents and the vector database are on your server. An external LLM (OpenAI or Mistral via API) is used to generate responses – but only anonymized text fragments, without file names or metadata, are sent to it. Suitable for most businesses without strict regulatory requirements. Lowest deployment cost.

Level 2: Closed Circuit with Local LLM. All components are on your server, including the language model via Ollama. No queries go outside. This is our standard choice for medicine, law, HR, and finance – where even anonymized text must not leave the perimeter. This is the level we deploy in most projects.

Level 3: Full Network Isolation (Air-Gapped). A server completely disconnected from the internet. The model and all dependencies are installed from a physical medium. Used in government agencies and the defense sector where any network traffic is a risk. Requires separate technical planning.

For the vast majority of business tasks, we recommend Level 2 – it provides a complete technical guarantee against data transmission to third-party AI services with reasonable server requirements and deployment costs.

How a Closed Circuit Differs from "Just Self-Hosted"

An important nuance that is often confused: self-hosted might mean your documents are stored with you – but the response generation still goes through an external LLM. This is Level 1 (Hybrid). A closed circuit is when storage, generation, and embedding all happen exclusively on your server. The difference is fundamental for regulated industries: even an anonymized fragment of a contract or protocol sent to an external API constitutes a legally problematic data transfer.

More on the difference and legal implications – in the article Self-hosted AI vs Cloud: Where Your Data Stays.

How Ollama Works and Why It's Suitable for Isolated Environments

Before Ollama, launching a local language model was a real technical challenge – even an experienced developer could spend a day just to get the model running. Now, it's resolved as part of a turnkey implementation. To understand the following sections, it's useful to know how it works technically.

What Ollama Does: Key Features

GGUF Format and Quantization. Ollama uses a compressed format where the model occupies 2–4 times less memory with minimal quality loss. Llama 3.3 70B in full format is ~140 GB. In Q4_K_M format, it's only ~43 GB with a 2–3% quality reduction. In practice, for business RAG tasks, this difference is unnoticeable.

Automatic Hardware Optimization. Ollama detects the presence of a GPU and distributes the load automatically. No GPU? The model runs on the CPU, slower but stable. This is important: you are not tied to specific hardware.

OpenAI-Compatible API. Ollama launches a local API server with an interface identical to the OpenAI API. To switch from OpenAI to Ollama, only one variable needs changing in the code – the server URL. No system logic rework is required.

Embedding Model Support. Ollama supports local embedding models – nomic-embed-text, mxbai-embed-large, BGE-M3. Document vectorization also happens locally – no external requests even when uploading new files.

Embedding Without Transmitting Data Externally: Why It Matters

Before AI can answer questions, it must "read" your documents. Each fragment is converted into a set of numbers (a vector) that encodes its meaning. This is what allows the AI to find an answer even if the question doesn't contain a single word from the document.

For businesses, a critical question is: where does this vectorization happen? If it's done via OpenAI API, your documents (even in fragments) are sent to US servers. For medicine and law, this is already a transfer of special categories of data without a legal basis.

For a closed circuit, we use local embedding via Ollama. mxbai-embed-large is the optimal choice for most business tasks: 1024-dimensional vectors, good quality on English and mixed documents. For predominantly Ukrainian or German documents, we recommend BGE-M3 – trained on 100+ languages with consistent quality for Latin and Cyrillic scripts.

Why Ollama is Suitable for Confidential Environments

• Offline AI Component Operation Post-Deployment: The model is downloaded once – after that, AI processing does not require internet connectivity to external services.
• No Telemetry: Ollama does not send query or usage data externally.
• Model Version Control: A specific version is fixed and does not change without your knowledge – unlike cloud APIs where the provider can update the model without notice.
• Docker Deployment: Ollama runs in a Docker container – simplifying backup, updates, and recovery from failures.

Available Local Models: Comparison and Industry Recommendations

Choosing a model for a closed circuit involves balancing three factors: response quality, server requirements, and speed. A smaller model answers faster but less accurately. A larger one is more accurate but slower and requires more powerful GPU. We select a model tailored to the client's specific scenario – there's no "one right answer" for everyone.

Current Models for Business via Ollama in 2026

Speed is for NVIDIA RTX 4090 (24 GB VRAM). Llama 3.3 70B requires either 48+ GB GPU or distribution between GPU and RAM.

Which Model to Choose Based on Industry

Law Firms and Notaries. High accuracy and long context for contract analysis are essential. We recommend Llama 3.3 70B – its 128K tokens of context allow processing a long contract entirely without splitting, which is critical for legal analysis. If the server is less powerful, Mistral Small 3 (24B) is a compromise: lower VRAM requirements with good quality on legal texts.

Medical Centers and Clinics. Accuracy when working with protocols and medical terms is a priority. We recommend Llama 3.3 70B or Gemma 4 26B – the latter offers GPT-4 level quality with VRAM requirements similar to a 16GB card. For clinics with multilingual documents (UA + DE), Qwen3 14B is suitable.

HR and Corporate Knowledge Base. Regulations, procedures, FAQs – typically simple structured texts where extremely high quality is not required. We recommend Llama 3.2 8B or Gemma 4 26B: the former for minimal hardware needs, the latter for higher quality with moderate VRAM requirements.

Distributors and B2B with Large Catalogs. Technical specifications and catalogs require precise data extraction. We recommend Mistral Small 3 (24B) – it understands technical terminology well, provides fast responses, and doesn't require a top-tier GPU.

Government Agencies. Maximum isolation, verified model source. We recommend Llama 3.3 70B from Meta – open license, public source, broadest community support, and independent security audits.

Multilingualism: How Local Models Handle Ukrainian, English, and German

For businesses in the Ukrainian and DACH markets, multilingualism isn't an option; it's a requirement. Documents can be in different languages, clients ask questions in their own way, and reports are prepared for partners. This is the real picture we consider when selecting a model.

English – The Base Language, Stable Quality Across All Models

All large models were trained primarily on English-language data. The quality of responses on English documents is excellent for all models in our table. If your documents are mostly in English, you can choose a model based solely on hardware requirements and speed.

Ukrainian – Good Quality with the Right Choice

The situation with Ukrainian has significantly improved in 2025–2026. Llama 3.3, Qwen3, and Mistral Small 3 contain a substantial portion of Ukrainian-language data.

• Llama 3.3 70B: Understands and responds well in Ukrainian. Minor grammatical errors do not affect the meaning of the response.
• Qwen3 14B: The best multilingual model in its class, including Cyrillic. Recommended when documents are predominantly in Ukrainian.
• Llama 3.2 8B: Understands Ukrainian but may mix languages in responses – requires explicit instructions in the system prompt.
• Embedding for Cyrillic: For maximum quality on Ukrainian documents, we recommend BGE-M3 – trained on 100+ languages with consistent quality for Latin and Cyrillic scripts.

German – Stable Quality for the DACH Market

German is represented in the training data of most models much better than Ukrainian. Llama 3.3, Mistral, and Qwen3 respond correctly and confidently in German.

• Mistral Small 3 (24B): Particularly strong in German – Mistral AI (a French company) paid significant attention to EU languages. Our choice for Austrian and German clients with monolingual DE documents.
• Medical and legal terminology in German is handled correctly by all leading models.

Cross-Lingual Search: Questions in One Language, Documents in Another

A typical business scenario: some documents are in English, some in Ukrainian, and questions come in any language. The embedding model, not the LLM, is crucial here.

BGE-M3 is our recommendation for multilingual archives: a Ukrainian query finds relevant fragments from English documents, and vice versa. This works because the multilingual embedding model places "договір" (contract) and "contract" in similar points in the vector space. It runs locally via Ollama: ollama pull bge-m3.

Practical Recommendation for UA/DE Businesses

Architecture: Server + pgvector + Ollama without Data Transfer to Third-Party AI Services

We at AskYourDocs explain the architecture not for the client to understand the technical details, but so they can confidently answer questions from a regulator or lawyer: "Where is the data stored? Who has access to it? Does the data leave the company's premises?" This is a straightforward answer in simple terms.

Four Components – What, Where, and Why

1. Server in the EU. A physical or virtual machine in a data center or your office. All other components reside on it. For GDPR compliance, the server is located in the EU and managed by a non-US company (Hetzner, OVH). For maximum isolation, a physical server located directly within your premises is recommended.

2. PostgreSQL + pgvector. A database where your documents are stored in two formats: the original text (for displaying the source in the answer) and vector representations (for search). pgvector is a PostgreSQL extension that adds vector search capabilities. No additional services, no cloud databases – just a single PostgreSQL server on your hardware.

3. Ollama. Runs the LLM and embedding model locally. It receives questions, finds relevant passages via pgvector, feeds them to the model, and returns the answer. The entire process occurs within your server, without any external AI requests.

4. Chat Interface or Telegram/WhatsApp Bot. This is what your employee or client sees and interacts with. The query is sent here, passed to the server, processed locally, and returned. Integration with Telegram and WhatsApp works via their Bot APIs, but the AI processing itself happens on your server.

How Data Moves: Step-by-Step

When uploading documents (one-time process):

1. You or your administrator uploads a PDF or Word document via the admin panel.
2. The system splits the document into chunks (~500 words each).
3. Ollama, using a local embedding model, converts each chunk into a vector.
4. The text and vector are stored in PostgreSQL + pgvector on your server.
5. The original file is saved to the server's disk.

At no point does the data leave your server.

When answering a question (each time):

1. An employee or client asks a question in the chat or Telegram bot.
2. The question is converted into a vector using the local embedding model.
3. pgvector finds the 3–5 most relevant document chunks.
4. The found chunks, along with the question, are passed to the local LLM (Ollama).
5. The model generates an answer with a source reference and returns it to the user.

The entire process occurs within the server. It takes 3–15 seconds depending on the model and hardware. No step requires an external AI service.

Minimum Server Requirements

Our recommendation for starting: for most business tasks (up to 1000 files, 50–200 queries per day), Llama 3.2 8B or Gemma 4 26B are sufficient. They run on a server with 16–32 GB RAM and a 16 GB GPU, or without a GPU (slower but stable). Llama 3.3 70B is needed when the quality of answers for complex legal or medical documents is a critical requirement.

Local Model Performance vs. GPT-4o: Real Numbers

The question we hear from every executive is: "If we use a local model, will the answers be worse than ChatGPT's?" The answer depends on the task, and for most business scenarios, it's positive.

Where Local Models Don't Fall Short Compared to GPT-4o

For RAG (answering questions based on your documents), the difference between Llama 3.3 70B and GPT-4o is minimal. The reason is simple: in RAG, the model doesn't "invent" an answer; it formulates it based on the retrieved fragments. With high-quality retrieval, even a smaller model provides accurate answers. The model's "general knowledge" is almost irrelevant here.

• Finding a specific clause in a contract or protocol: Llama 3.3 70B ≈ GPT-4o
• Extracting structured information: Identifying parties, dates, terms in a contract – the local model performs just as well.
• Document summarization: Concise retelling – practically equivalent quality.
• FAQ on regulations and procedures: "What is the procedure for business trips?" – the local 70B gives equally accurate answers.

Where GPT-4o Has a Real Advantage

• Complex multi-document analysis: Synthesizing legal positions from multiple sources, identifying contradictions between documents – GPT-4o is more accurate.
• Generating new documents "from scratch": Writing a memo or report – GPT-4o is of higher quality.
• Complex mathematics and logical chains: Financial calculations – cloud models are more accurate.

If these scenarios are relevant to your business, we recommend a hybrid approach: 80–90% of queries are processed locally, with complex analysis involving non-sensitive data handled via a cloud API.

Real-World Speed Metrics

Practical takeaway: 10–15 seconds for Llama 3.3 70B on a GPU is acceptable for 80% of corporate scenarios. A manager who previously spent 20 minutes manually searching documents now gets an answer in 15 seconds – an 80x acceleration. The difference between 15 and 3 seconds is insignificant in this context. For public-facing chatbots where response speed is crucial, we recommend Gemma 4 26B or Mistral Small 3: they respond in 5–8 seconds with sufficient quality for FAQs and procedural questions.

Our Testing on Real Business Documents

We tested Llama 3.3 70B and GPT-4o on the same dataset: 400 legal documents (contracts, regulations). Tasks included finding a specific clause, checking for the existence of a condition, and extracting contract parties. Result: Llama 3.3 70B achieved 91% correct answers, while GPT-4o achieved 94%. This 3 percentage point difference is acceptable for most business applications and is fully offset by the absence of GDPR risk and zero API costs.

Hardware and Server Costs: The Price of a Closed System

Option 1: Cloud VPS with GPU (Recommended for Getting Started)

The easiest approach is to rent a VPS with a GPU from a cloud provider in the EU. Advantages: no need for your own hardware, the provider is responsible for the equipment, easy to scale. Important for GDPR: we exclusively use providers under EU jurisdiction, outside the reach of the US CLOUD Act. We do not recommend Vast.ai and similar GPU marketplaces for business data, as the identity of the node operator and the actual data location are not guaranteed.

⭐ All providers in the table are in the EU jurisdiction, outside the US CLOUD Act. Hetzner (Germany), OVHcloud (France), Scaleway (France) are ISO 27001 certified.

Option 2: Own Physical Server (Maximum Isolation)

If data must absolutely not leave your premises (air-gapped requirement) or you wish to be completely independent of a cloud provider, an in-house server is the solution. We deploy Ollama and the entire stack on your hardware, giving you a system that is physically never connected to external AI services.

Total Annual Cost Comparison

Our conclusion on costs: for companies without strict regulatory requirements, the hybrid mode offers the cheapest starting point. A closed system with a GPU costs more, but for healthcare, legal, and finance sectors, it's the only option where GDPR risk is technically absent. A single regulatory inquiry or fine can easily exceed the server cost difference over several years. We always calculate the total cost with the client, including the legal aspects, before recommending a specific level of isolation.

When a Closed-Loop System Isn't Optional, But Mandatory

We at AskYourDocs observe a pattern: most clients who opt for a closed-loop system do so not out of paranoia, but due to specific regulatory or reputational requirements. Here's when a closed-loop system is not an option, but the only acceptable solution.

Medical Centers and Clinics

Medical data is a special category under GDPR Art. 9, requiring the highest level of protection. Any transmission of medical records, patient inquiries, or treatment protocols to a third party without explicit consent and a legal basis is a direct violation. Even a question like "How to prepare for chemotherapy?" reveals a cancer diagnosis and constitutes Art. 9 data. In Austria, medical confidentiality is protected by criminal law (Ärztegesetz § 54), and the Data Protection Authority has established the strictest standard in the EU regarding data transfers to the US.

Our stance: For medical centers, we do not implement a hybrid mode; only a closed-loop system on an EU server. More details can be found in the article AI in Medicine: How to Process Medical Data Without Breaking the Law.

Law Firms and Notaries

Attorney-client privilege is a fundamental legal principle. Uploading client case materials to ChatGPT or Notion AI without explicit client consent violates attorney-client privilege, regardless of whether anyone actually viewed the data. The CCBE (Council of Bars and Law Societies of Europe) explicitly warned against cloud-based GenAI systems in 2025. A US federal court ruled in February 2026 that materials prepared using public AI tools are not protected by attorney-client privilege.

Our stance: A closed-loop system eliminates the very possibility of "transfer to a third party" — attorney-client privilege is protected architecturally, not just on paper. More details can be found in the article AI for Law Firms: Client Data Security.

Government and Municipal Institutions

Processing citizens' personal data on servers owned by American companies is de facto prohibited in most EU countries due to data sovereignty requirements. The CLOUD Act allows US law enforcement agencies to demand access to data held by AWS, Azure, and Google Cloud, regardless of server location. For most government bodies, a closed-loop system on national or EU infrastructure is the only option; for some, complete network isolation (air-gap) is required.

Financial Institutions and Insurers

BaFin (Germany) and FMA (Austria) have clear requirements for financial data processing channels and mandatory approval for outsourcing to third-party providers. Credit files, client accounts, and insurance policies cannot be transferred to servers outside the designated jurisdiction without regulatory permission. A closed-loop system on an EU server resolves this systematically without a separate regulatory process.

HR Departments and Employee Data Processing

Employee personal data—performance reviews, salaries, medical examinations, disciplinary records—fall under enhanced protection requirements. Employees have not consented to their data being transferred to OpenAI or Notion. Every HR manager's query with this data via cloud AI constitutes a potential GDPR Art. 6 violation. A closed-loop system resolves this issue architecturally.

Companies with B2B Client or Partner Requirements

Your clients or partners may explicitly demand confirmation that their data is not processed on third-party servers. ISO 27001 or SOC 2 certifications from a cloud AI provider do not serve as such confirmation. Technical documentation of a closed-loop system and architectural guarantees of no external queries do.

More on all these scenarios and legal requirements can be found in the article 6 Risks of Data Leakage via AI: How to Protect Your Business in 2026.

How We Deploy a Closed-Loop System: A Step-by-Step Plan

This plan is written for business leaders, not developers. Your IT department is not required.

Step 1: Document Preparation — Your Task (1–2 days)

Before system deployment, your documents need to be ready for upload:

• Format: PDFs with a text layer (not scans), Word (DOCX), Google Docs, or TXT. Scanned PDFs without OCR are unreadable by AI and need to be converted beforehand.
• Up-to-date: Only current versions. Outdated regulations will yield outdated answers. Please verify the current status before uploading.
• Starting Volume: 50–200 files are sufficient. The system works equally well with 10 or 10,000 documents.

For more details on document preparation, see the article How to Prepare Documents for an AI Assistant.

Step 2: Server Selection and Setup — Our Task (1 day)

We will choose the configuration together with you, considering the number of documents, workload (daily queries), desired response quality, and GDPR level.

• For most businesses: A VPS with GPU at Hetzner (Nuremberg or Finland) or OVHcloud (Strasbourg). A server in the EU managed by a non-US company inherently meets GDPR compliance.
• For maximum isolation: A physical server at your office or data center.
• Installation of Ubuntu, Docker, Ollama, PostgreSQL + pgvector is an automated process taking 2–3 hours.

Step 3: Model Download — Our Task (Several Hours)

• Download LLMs via Ollama based on the chosen configuration (e.g., Llama 3.3 70B Q4 — approx. 43 GB, takes 30–60 minutes).
• Download embedding model (nomic-embed-text or BGE-M3 — approx. 0.5–1.5 GB).
• Configure the system prompt: response language, format, constraints (e.g., answer exclusively based on your documents).
• Once configured, AI processing does not require external AI services. Telegram or web chat for user access connects via their standard protocols.

Step 4: Document Upload and Vectorization — Our Task (1–4 hours)

• Upload your documents via the admin panel or an automated script.
• The system automatically segments each document, vectorizes it using a local embedding model, and stores it in pgvector.
• Processing time: approx. 100 documents in 15–30 minutes on a standard server.

Step 5: Interface Setup — Our Task (1 day)

• Web chat widget to embed on your website (a single line of code).
• Telegram bot or WhatsApp bot for internal team use.
• Origin filter: chat accessible only from your authorized domains or IP addresses.
• Configure query logging for analytics and auditing (optional).

Step 6: Testing and Handover — Together (30 minutes)

• You ask 10–15 real-world questions based on your documents and verify the quality of the answers.
• We adjust settings if the quality is not satisfactory—until you confirm.
• Transfer of all administrator access: server, database, admin panel.
• After handover, we will no longer have technical access to your server. This is not a promise, but an architectural guarantee.

After Launch: How Document Updates Work

Upload a new or updated file via the admin panel → the system automatically vectorizes it → within 2–3 minutes, the assistant responds based on the new document. No IT department is needed. Post-launch support is included for 30 days, then by agreement starting from $50/month.

Frequently Asked Questions

Can the system operate entirely without external AI services?

Yes—after initial setup, AI processing requires no external AI services. For Telegram or WhatsApp integrations, minimal traffic is used to the messenger's servers, but not to AI providers (OpenAI, Anthropic, etc.). If complete network isolation (air-gap without internet access) is required, the system is deployed with a web interface within your local network, with no external connections.

How difficult is it to update documents?

Very simple. Upload a new or updated document via the admin panel (drag-and-drop) → the system automatically processes and vectorizes it. Within 2–3 minutes, the assistant responds based on the new document. The old version is automatically replaced if the name is the same. No IT knowledge is required—any administrator can handle it.

What happens if the server goes down?

With cloud VPS (Hetzner, OVH), there's an automatic restart via Docker, typically with 2–5 minutes of downtime. For a physical server in your office, we configure auto-start upon power restoration. We set up automatic database backups to an external encrypted disk to protect against hardware failure.

How many users can work with the system concurrently?

This depends on the model and hardware. For Llama 3.2 8B or Gemma 4 26B on a 16 GB GPU, 5–10 concurrent requests can be handled without noticeable slowdown. For Llama 3.3 70B, it's 2–3 concurrent requests. For higher loads, a request queue or a more powerful server is needed. For most office tasks (100–200 queries per day), the standard configuration is sufficient.

Is it possible to switch from a hybrid mode to a closed-loop system later?

Yes, and this is one of our most common scenarios. Businesses start with a hybrid mode (lower initial cost) and transition to a closed-loop system as volumes increase or stricter GDPR requirements arise. Migration takes 1–2 days: documents are already in the system; you just need to replace the external LLM with the local Ollama and switch to local embeddings. No business logic redesign is required.

Conclusion

• 🏠 Closed-Loop System: The only solution where data cannot technically leave your perimeter—not through provider promises, but through architecture.
• 🦙 Ollama: Has made local LLMs accessible for businesses—we provide turnkey deployment without requiring a client-side IT department.
• 📊 Quality: Llama 3.3 70B achieves 91% accuracy on RAG tasks compared to GPT-4o's 94%—a 3% difference is acceptable and offset by the absence of GDPR risk and zero API costs.
• 🌍 Multilingual: Ukrainian, English, and German are supported. For Cyrillic characters, Qwen3 14B and BGE-M3 embeddings yield the best results.
• 💰 Cost: From €30/month (CPU-only) to €350/month (24 GB GPU)—comparable to or cheaper than Enterprise cloud plans when considering legal expenses and GDPR risks.
• ⏱️ Implementation: 5–7 business days turnkey. After handover, we no longer have access to your server.
• 🏥 Mandatory for: Medicine (GDPR Art. 9), Lawyers (attorney-client privilege), Government bodies (data sovereignty), Finance (BaFin/FMA requirements).

Want to Deploy a Closed-Loop System?

Show us your documents and your task—in a 30-minute demo, you'll see how AI answers real questions from your archive. And where your data is physically located during this process.

Write on Telegram →

Want to see the solution in action? askyourdocs.org/en/#try-demo

Turnkey implementation in 5–7 days. No IT department required. Data remains on your server in the EU. Learn more about GDPR in the article GDPR and AI on Documents: What Businesses Should Know in 2026.

Read also

• Self-hosted AI vs Cloud: Where Your Data Resides
• GDPR and AI on Documents: What Businesses Should Know in 2026
• 6 Risks of Data Leakage via AI: How to Protect Your Business in 2026
• AI for Law Firms: Client Data Security
• AI in Medicine: How to Process Medical Data Without Breaking the Law
• All Articles on Data Security and AI

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Sources: Ollama Model Library · Best Ollama Models: Performance Comparison · Local AI Models Directory 2026 · The State of Local LLMs 2025–2026 · What are Embeddings: How AI Understands Meaning, Not Just Words