Llama

Llama is Meta’s family of open-weight large language models, and it is arguably the most important project in the entire open AI ecosystem. While ChatGPT, Claude, and Gemini are closed products you can only rent through an app or an API, Llama is different in kind: Meta publishes the actual model weights, and anyone can download them, run them on their own hardware, fine-tune them on their own data, and ship them inside their own products. That single decision has done more to democratize advanced AI than almost anything else in the field.

It is important to be clear about what Llama is and is not. Llama is not a chatbot you log into; that consumer assistant is Meta AI, which is built on top of Llama. Llama itself is the engine: the raw models that power Meta AI, thousands of startups, research labs, and self-hosted enterprise deployments around the world. If you have used an AI feature anywhere in the last few years, there is a real chance Llama was running underneath it.

This guide explains everything that matters about Llama in 2026: what open-weight actually means and why it is a big deal, the Llama 4 model lineup of Scout, Maverick, and Behemoth, how the mixture-of-experts architecture works, the license and its fine print, how to actually get and run the models, and where Llama fits against the closed competition. By the end you will understand why Llama matters even if you never download a single weight file.

What Is Llama?

Llama (originally styled LLaMA, for "Large Language Model Meta AI") is a series of foundation models developed by Meta. Unlike the closed models behind most popular assistants, Meta releases Llama as open-weight: the trained parameters are made freely available for download, so developers and organizations can run the models themselves rather than calling a remote API they do not control.

This matters enormously. Running a model on your own infrastructure means your data never leaves your servers, a decisive advantage for healthcare, finance, government, and any organization with strict privacy or compliance requirements. It also means no per-token bills to a third party, no vendor lock-in, the freedom to fine-tune the model deeply for a specific domain, and the ability to keep running it indefinitely on your own terms. For an entire generation of AI startups, Llama is the foundation they built on precisely because it gave them that control.

Meta’s strategy here is deliberate. By open-sourcing the weights, it has made Llama the default base model for a vast community of builders, researchers, and companies, accelerating innovation, attracting talent, and ensuring the broader ecosystem is built around Meta’s models rather than a competitor’s closed platform.

The Llama 4 Herd

The current generation, Llama 4, arrived in 2025 and marked a major leap. It is the first Llama generation that is natively multimodal (trained on text and images together) and the first to use a mixture-of-experts (MoE) architecture. Meta released it as a "herd" of models with very different shapes for very different jobs.

Most builders will use Scout or Maverick, since they are the practical, deployable open-weight models. Scout’s gigantic context window is the headline feature for document-heavy and long-memory applications, while Maverick is the balanced default for general assistants and products.

What Mixture-of-Experts Means

The MoE architecture is the clever engineering trick behind Llama 4’s efficiency. Rather than activating every parameter for every token, an MoE model is divided into many specialized "experts," and a router sends each piece of input only to the few experts best suited to handle it. That is why Scout and Maverick are described as having "17B active parameters": only a fraction of the total model runs on any given token. The payoff is models that punch far above the compute cost of their size, making powerful AI cheaper to run.

The License: "Open Weight," Not Quite "Open Source"

It is worth being precise about Llama’s openness, because it is often overstated. Llama is released under the Llama 4 Community License Agreement, which is genuinely permissive: you can download, run, modify, fine-tune, and commercialize the models for free. For the overwhelming majority of developers and companies, it is effectively as good as open source.

However, it is not a standard OSI-approved open-source license. It carries a few restrictions: very large companies (above a high monthly-active-user threshold) must request a separate license from Meta, there is an acceptable-use policy governing harmful applications, and products built on Llama are expected to attribute it. So "open-weight" is the accurate term: the weights are open and free for nearly everyone, with guardrails aimed at Meta’s largest competitors rather than ordinary builders.

How to Access and Run Llama

Because Llama is open-weight, there is no single "Llama app." Instead, there are many ways to get and use the models depending on your needs and technical comfort.

• Download the weights directly from llama.com or from model hubs like Hugging Face, then run them on your own GPUs or servers.
• Run locally with one click using tools such as Ollama, LM Studio, or llama.cpp, which let you run smaller Llama models on a capable laptop or desktop, fully offline and private.
• Use a cloud provider: every major cloud and inference platform (and many specialized ones) hosts Llama models behind an API, so you can use them without managing hardware.
• Build on Meta’s own tools like the Llama API and the Llama Stack, which package the models with the supporting components needed for production applications.
• Just use a product built on it, including Meta’s own Meta AI assistant, which runs on Llama under the hood.

How Llama Compares to Closed Models

Comparing Llama to ChatGPT, Claude, or Gemini is a little like comparing an engine you own to a car service you subscribe to. The right choice depends on whether you value convenience or control.

The takeaway: if you want a finished assistant to just use, a closed product is simpler. If you are a developer or an organization that needs privacy, control, customization, or freedom from per-call billing, Llama is the foundation to build on. The two worlds are not really competitors so much as different layers. Many products use a closed model for some tasks and a self-hosted Llama for others.

Real-World Use Cases

For Startups and Product Builders

Countless AI startups build their products on Llama because it gives them a capable model without a dependency on a competitor’s API, predictable costs at scale, and the freedom to fine-tune for their specific use case. Owning the model is a strategic advantage when AI is the core of your product.

For Enterprises With Privacy Requirements

Hospitals, banks, law firms, and government agencies often cannot send sensitive data to a third-party API at all. Running Llama on their own infrastructure lets them deploy advanced AI while keeping every byte of data inside their own walls, frequently the only way these organizations can adopt generative AI compliantly.

For Researchers and the Open Community

Because the weights are open, academics and independent researchers can study, probe, and improve the models directly, work that is impossible with closed systems. Llama has become the backbone of a huge volume of open AI research and the parent of thousands of community fine-tunes specialized for languages, domains, and tasks.

Limitations to Keep in Mind

Final Verdict

Llama is the quiet giant of the AI world. It is not the assistant most people talk to directly, but it is the foundation an enormous slice of the industry is built on, and by giving away the weights, Meta has made frontier-class AI accessible to anyone with the skills to use it. For developers, startups, researchers, and privacy-conscious enterprises, that combination of capability, control, and zero licensing cost is unmatched by any closed product.

If you just want to chat with an AI, you want a finished product, not Llama itself. But if you want to build on, own, and control your AI, Llama is the single most important open foundation available, and the reason the open AI ecosystem exists at the scale it does. Exploring what to build with it? Browse more free AI tools to round out your stack.