Ollama: AI Runs Locally, Your Data Never Leaves
"Our data cannot leave the company." We hear this from every enterprise client. Ollama let us say "no problem" — running powerful LLMs directly on the client's server, with no cloud APIs, no subscriptions, no compromises.
Cloud APIs and Sensitive Data Don't Mix
When we use ChatGPT or Claude, every message travels to servers in the United States. For personal use that's fine. For a law firm with confidential documents? For a hospital with medical records? For a company with not-yet-filed patents? Absolutely not. GDPR is clear: EU citizens' personal data has precise rules about where and how it's processed. Sending confidential contract content to cloud services exposes the data controller to concrete legal risks. We're not talking paranoia — we're talking compliance. Our clients didn't want to choose between "using AI" and "protecting data". They wanted both. Ollama gave us the answer: run the AI model directly on your server, data never leaves.
How Ollama Works (In Practice)
Ollama is an open-source LLM runtime that installs in 30 seconds. A single command — "ollama pull llama3.1:8b" — downloads a 4.7 GB model and makes it available via local REST API on port 11434. No manual CUDA configuration, no PyTorch compilation. Under the hood, Ollama uses llama.cpp (optimized C++) for inference. It automatically detects the NVIDIA GPU and moves the model to VRAM. If the GPU doesn't have enough memory, it splits the model between GPU and CPU. Models we use in production: - Llama 3.1 8B: our workhorse. 4.7 GB, runs on any GPU with 8 GB VRAM. Generates 40-60 tokens/second on RTX 4060. - Mistral 7B: good Italian and English performance. Slightly faster than Llama on short texts. - Phi-3 Mini: 3.8 GB, perfect for limited hardware. Excellent for factual Q&A.
Local vs Cloud Performance: The Honest Comparison
We'd be dishonest to say local Llama 3.1 8B is as good as GPT-4o. It's not. But the right question isn't "which model is smarter?" — it's "which solves the client's problem within their constraints?" On our benchmark of 100 questions on real legal documents: - GPT-4o (via API): 94% accurate, 1.2 seconds, cost $0.03/query - Llama 3.1 8B local: 87% accurate, 2.1 seconds, cost $0.00/query - Llama 3.1 70B local (2x RTX 4090): 92%, 4.8 seconds, $0.00/query Llama local's 87% is on complex cross-document questions. On simple factual questions, Llama 8B reaches 98%. A company doing 1,000 queries/day saves about $900/month vs GPT-4o, after the initial investment of a €300 RTX 4060.
FAQ About Ollama and Local LLMs
Q: Do you absolutely need an NVIDIA GPU for Ollama? A: No, it works on CPU too. But it's slow: 3-5 tokens/second vs 40-60 on GPU. For enterprise use with multiple users, a GPU is practically mandatory. Q: Are local models safe? Can they be manipulated? A: Models come from verified repositories. The real risk is prompt injection, but that applies to any LLM. Our system mitigates with input validation and response sandboxing. Q: Can I fine-tune on my company data? A: Ollama supports custom models, but actual fine-tuning requires external tools. In our experience, RAG with good prompt engineering covers 95% of cases. Fine-tuning is only for highly specialized domains.
Related Services
See how we apply these technologies in our enterprise projects.
Interested?
Contact us to receive a personalized quote.
Securvita S.r.l. — i3k.eu