What Is a Large Language Model (LLM)?
A large language model (LLM) is a type of artificial intelligence trained on billions of words of text to understand, generate, and reason about human language. Models like GPT-4, Claude, Gemini, and Llama are all LLMs.
How Do LLMs Work?
LLMs are built on the transformer architecture. During training, the model learns statistical patterns across massive text corpora — books, websites, code, and more. This allows it to:
- Generate coherent, contextually relevant text
- Summarize long documents
- Translate between languages
- Write code in dozens of programming languages
- Reason through multi-step problems
Key Concepts
- Parameters: The learned weights that define the model’s knowledge. GPT-4 is estimated to have over 1 trillion parameters.
- Tokens: LLMs process text as tokens — subword units that the model reads and generates.
- Context window: The maximum amount of text an LLM can process in a single conversation.
- Temperature: A setting that controls how creative or deterministic the model’s output is.
Popular LLMs
| Model | Provider | Key Strength |
|---|---|---|
| GPT-4o | OpenAI | General-purpose reasoning |
| Claude | Anthropic | Long context, safety |
| Gemini | Multimodal (text + images) | |
| Llama | Meta | Open-source, local deployment |
| Mistral | Mistral AI | Efficient, open-weight |
Running LLMs Locally
With tools like Ollama, you can run open-source LLMs directly on your Mac without sending data to the cloud. Elvean connects to both local models via Ollama and cloud APIs — giving you the best of both worlds in one native app.
Elvean brings all these concepts together in one native Mac app — local models, cloud APIs, agentic tools, and more.
Learn more about Elvean