Large Language Models are neural networks with billions of parameters that learn statistical language patterns by training on enormous text corpora. They can generate, summarize, translate, classify text and answer questions — without being separately programmed for each task.

The most well-known LLMs are proprietary cloud models: GPT‑4 (OpenAI), Claude (Anthropic), Gemini (Google). For enterprise use with sensitive data, these models have a critical disadvantage: prompts and data leave the organization and are transferred to external servers — a data transfer that is problematic under and subject to the US CLOUD Act when the provider is a US company.

Alternatively, open-source LLMs (Llama, Mistral, Falcon and others) can be operated locally. Local operation requires dedicated GPU hardware, as LLM inference is computationally intensive. The advantage: data never leaves the network. Silent AI uses local LLMs on dedicated GPU hardware (Nvidia A6000 Pro Blackwell) — combined with a RAG architecture over internal enterprise data.

Frequently asked questions

The LLM is the core model — the neural network that understands and generates language. An AI assistant like ChatGPT is a product that combines an LLM with a user interface, safety filters, user management and (in enterprise use) additional services. When an organization runs an LLM locally, it has the model under its own control — without dependency on an external service provider.
No. After initial deployment, the model runs completely offline. Neither prompts, nor responses, nor source data leave the network. This is the structural difference from cloud AI services: Silent AI is fully operable without network connectivity after installation.