Meta's Llama 4 in Detail: An Overview of Scout, Maverick, and the MoE Architecture
A detailed examination of the llama-models repository reveals the technical specifications for Meta’s Llama 4 series, with a specific focus on the Scout and Maverick models.
What happened?
Analyses of code and model cards in the official llama-models repository reveal the technical specifications of Meta's Llama 4 series. The model family is built on a Mixture-of-Experts (MoE) architecture with multimodal capabilities for text and images using so-called early fusion. Two primary configurations have been specified: Llama 4 Scout (17B×16E) with 16 experts and Llama 4 Maverick (17B×128E) with 128 experts, both based on a base parameter volume of 17 billion parameters.
Key facts
| Lanseringsdatum | April 2025 |
|---|---|
| Llama 4 Scout arkitektur | 17B × 16E (16 experter) |
| Llama 4 Maverick arkitektur | 17B × 128E (128 experter) |
| Utvecklare | Meta |
Why it matters
Llama 4 marks a significant step for open AI models by integrating image and text understanding directly into the model's input stage instead of using separate OCR or image analysis steps. The MoE architecture ensures that only a subset of experts is activated per token, providing high performance at a significantly lower computational cost compared to traditional dense models.
Who is affected?
Developers, AI researchers, and companies building their own AI applications are directly affected. Through access to optimized MoE models and support for FP8 and INT4 quantization, it becomes possible to run advanced multimodal AI systems on local or cost-efficient hardware.
Impact on the EU
As Llama 4 is released with open source code and open model weights, the models are available to developers within the EU. However, organizations implementing Llama 4 in Europe must ensure compliance with the EU AI Act and GDPR, particularly regarding transparency concerning training data and the handling of personal data.
What else you should know
The source code and model cards in the llama-models repository provide deep insights into how the future of open AI infrastructure is being built. By offering both smaller and larger MoE configurations, Meta is challenging closed commercial models in the market.
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