Llama Models in Detail: From Llama 2 to Llama 4 and What Open Source Really Means for AI

Anyone who only knows the Meta AI app or the chat field in WhatsApp is only seeing the surface. Underneath it works a family of AI models called Llama, which Meta has released in several generations since 2023. What makes it special: unlike ChatGPT, Claude or Gemini, practically anyone can download the so-called model weights for Llama and run the model on their own hardware, instead of necessarily going through a provider’s servers. This article explains what that concretely means, how the Llama versions differ, and where this openness actually makes a difference in everyday life and at work.

What does “open source” or “open weight” actually mean for an AI?

The difference from ChatGPT, Claude and Gemini

With most well-known AI chatbots such as ChatGPT from OpenAI, Claude from Anthropic or Gemini from Google, the actual computation runs exclusively on the respective provider’s servers. You type in a question, the request travels over the internet to the provider’s data center, is processed there, and the answer comes back. The model itself – the huge file containing the trained “thinking patterns” – always stays with the provider and cannot be viewed or downloaded by anyone outside.

With Llama, it’s different. Meta releases the trained model weights for download. Anyone with the technical prerequisites can install and run Llama on their own server, in their own cloud environment, or even on a capable PC, without a single request ever being sent to Meta’s servers. That’s exactly why Llama is more precisely described as “open weight” rather than fully classic open-source software: the trained results are openly viewable and usable, whereas the exact training data and the complete training process are not fully public.

What does that concretely mean: self-hosting instead of using a cloud service?

“Self-hosting” means that a company or a tech-savvy private individual sets up the software so that the AI runs on its own hardware, whether that’s an in-house server in the office, a rented server at a German data center, or a private cloud. The big advantage: in this case, all data entered into the AI never leaves your own infrastructure. There is no third-party company reading, storing, or analyzing the requests for its own purposes. This is the decisive structural difference from Meta AI as a finished product, which always runs on Meta’s own servers, or from ChatGPT and Claude, where self-hosting is fundamentally not technically possible.

An open-weight model like Llama is like a recipe you can download and cook yourself in your own kitchen. A classic cloud chatbot is like a restaurant you have to keep going back to, because you never get to see the recipe.

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The Llama generations at a glance

Llama 1 and 2: the beginnings

The first Llama version from spring 2023 was initially intended only for research purposes and, above all, ensured that open AI models were taken seriously in the first place. With Llama 2 in summer 2023, Meta explicitly opened up use for commercial purposes for the first time, laying the foundation for a broad developer community building on Llama.

Llama 3: the breakthrough into the mainstream

With Llama 3 in 2024, Meta above all improved the quality of responses, multilingual support, and the ability to handle significantly longer texts. Llama 3 is considered the version with which open models were first able to seriously compete with the best closed models, and it became the foundation for numerous business applications.

Llama 4: Scout, Maverick and Behemoth

The current generation, Llama 4, arrived in April 2025 and brought an important technical leap: the models are built from the ground up to be “multimodal,” meaning they understand not only text but also images within a single model. The family consists of several sizes: Llama 4 Scout is the more compact, more efficient variant for simpler tasks and use with limited hardware. Llama 4 Maverick is the more powerful all-round variant for more demanding tasks. Llama 4 Behemoth is by far the largest model in the family and initially served mainly as an internal “teacher model” used to train the smaller variants; whether and when Behemoth would become publicly available as a standalone model was still not fully settled as of mid-2026.

The Llama license: what you should know

Even though Llama is often called an “open-source model,” strictly speaking that’s not entirely accurate. According to the official definition of open-source software, use would have to be completely unrestricted, which is not the case with Llama. Meta does allow commercial use very broadly, with one important exception: anyone operating a product or service with more than 700 million monthly active users needs a separate license directly from Meta. For the vast majority of companies, including larger mid-sized businesses or real estate companies, this threshold is, in practice, completely irrelevant, since they are orders of magnitude below it. Still, it’s important to know the exact license terms before anchoring Llama firmly into a product of your own.

For almost every normal company in Germany, the 700-million-user license threshold is purely theoretical. The more relevant question is how and where you run Llama, not whether you’re legally allowed to.

Examples of use from everyday life and work

Example 1: Everyday questions in chat

The simplest use happens via Meta AI itself: a question about a recipe, a translation, a short summary of a text. Here, Llama runs in the background on Meta’s own servers, without the user noticing anything about it at all.

Example 2: Developers building their own applications

A software company can download a Llama model and build it into its own app, for example a customer service chatbot, without paying an external cloud provider for every single request. The ongoing costs then consist of the company’s own server rather than a per-request bill.

Example 3: Sensitive data in the real estate and finance industry

A practical example: a real estate agency wants to create listing exposé texts, property descriptions or internal notes on prospective buyers with AI support, without customer data, purchase prices or draft contracts being transferred to an American cloud provider. With a self-operated Llama model on a server in Germany, this information stays entirely within the company’s own infrastructure. This is not a substitute for full data protection compliance, but it is a structural difference from a cloud chatbot, where data fundamentally leaves the building. Importantly: the effort involved in setting up, maintaining and updating your own model is real and should be realistically planned for before deciding to go this route.

Example 4: Translation and multilingual support in public authorities and administration

Public authorities or larger case-processing teams sometimes use open models like Llama to pre-sort multilingual inquiries or draft standard replies, without transmitting citizens’ data to an external cloud service.

Example 5: A simple calculation example on cost logic

Suppose a small company has around 50,000 AI requests processed monthly for text creation. Via a commercial cloud chatbot billed per request, depending on the provider and model size, several hundred euros a month can quickly add up, since costs rise with usage volume. A self-operated server with a more compact Llama model like Scout, on the other hand, incurs largely fixed costs for renting and running the server, regardless of whether 10,000 or 80,000 requests are processed in a month. Beyond a certain usage volume, self-hosting can therefore become cheaper than ongoing per-request billing – but only if the necessary technical expertise for setup and maintenance is already available or is brought in. For small, irregular use, a cloud service often remains the more practical choice.

FAQ

Is Llama completely free to use?

Downloading and using the model weights is free for the vast majority of use cases. Costs arise from operation, i.e. from server hardware or cloud computing power needed to run the model.

Do I need programming skills to run Llama myself?

Running it independently generally requires technical expertise or an IT service provider. For normal use via Meta AI in WhatsApp or the app, on the other hand, no prior knowledge is needed.

Is a self-operated Llama model automatically GDPR-compliant?

No, nothing is automatic. However, self-hosting creates the technical precondition for data not having to leave the building, which is an important basis for privacy-friendly solutions. The concrete legal implementation nevertheless remains necessary.

What is the difference between Llama 4 Scout and Maverick?

Scout is the more compact, resource-efficient variant for simpler tasks and smaller servers; Maverick is the more powerful variant for more demanding applications with correspondingly higher hardware requirements.

Can every company use Llama in its own products?

For the vast majority of companies, yes, within the framework of the Llama license terms. Only for services with more than 700 million monthly active users is a separate agreement with Meta required.

Anyone who, after this technical deep dive, wants to look once more at the finished product Meta AI in everyday use will find the overview on the provider page for Meta. A comparison with the other major AI providers is available on the overview page of the AI models cluster.