Llama in one minute
A model with available weights can run outside the consumer app, be adapted or be studied where its licence allows. This requires software and computing resources. Open weights do not automatically mean that all training data is public or that every use is unrestricted.
From research to a developer ecosystem
The first LLaMA publication appeared in 2023 with a research focus. Later generations broadened access for developers and businesses. The range includes small models for more limited hardware and larger models for demanding applications.
The current official download page includes Llama 4 Scout and Maverick. Older versions may still be useful in existing systems. A newer model is not automatically easier to run locally; its architecture, size and implementation determine the resources required.
Open weights allow researchers to measure behaviour and developers to build their own services. This makes the ecosystem broader than one chat website. The licence remains part of the decision: downloadable does not mean free of conditions.
What does local deployment mean in practice?
With local deployment, your own computer or server performs the calculations. Memory requirements depend on model size, context and possible quantisation: reducing numerical precision. Smaller files may fit more easily, but quality needs to be checked for the intended task.
Example: an agency wants to sort existing product descriptions into fixed categories. A suitable Llama model can suggest classifications inside its own environment. A person or a fixed check compares them with the catalogue. Current stock levels still require access to current business data.
Running locally does not by itself prevent data leaks. External search tools, logging or a connected cloud service may still transmit information. The complete application determines where data goes. Maintenance, updates and computing time remain necessary.
Is Llama still the engine behind Meta AI?
Not as a general current assumption. Llama played a central role in earlier Meta AI versions, but Meta announced Muse Spark as a new foundation for Meta AI in 2026. On 7 October 2026, the Llama ecosystem must therefore be distinguished from current product development with Muse.
This does not mean every Meta product and country uses exactly the same backend. Nor does it mean Llama has no continuing role: developers, researchers and existing implementations can use the available models within their terms.
For marketing, the choice is concrete: do you want a ready-to-use assistant or a custom application with control over execution and integration? Llama primarily belongs to the second question. A local model does not itself improve Google rankings; the quality of published information remains what matters.