Unveiling the Llama 4 Group in Azure AI Foundry and Azure Databricks
Introduction to Llama 4 Models
We are thrilled to announce that the first models of the Llama 4 series are now available through Azure AI Foundry and Azure Databricks. These offerings allow developers to create more tailored multimodal experiences. The latest models from Meta focus on merging text and visual tokens into a single model. This innovative structure empowers users to utilize the Llama 4 models in projects that require processing substantial amounts of unstructured data such as text, images, and videos.
Llama 4 Models: Key Offerings
Today, we feature the introductory models in the Llama 4 range, which include:
Llama 4 Scout Models
- Llama-4-Scout-17B-16E
- Llama-4-Scout-17B-16E-Instruct
Llama 4 Maverick Models
- Llama 4-Maverick-17B-128E-Instruct-FP8
The Llama 4 models are designed to support multi-agent applications and foster collaboration among different AI agents. This opens the door to exciting new possibilities in AI, enabling complex problem-solving and dynamic task management.
Llama 4 Scout: Power and Precision
The Llama 4 Scout is recognized as one of the top multimodal models currently available. It significantly surpasses the capabilities of its predecessor, Llama 3, while fitting comfortably within the limits of a single H100 GPU. A notable improvement in Llama 4 Scout is the extended context length, which now supports up to 10 million tokens. This is a game-changing feature that allows for advanced tasks such as:
- Multi-document summarization
- Personalized task parsing based on extensive user data
- Complex reasoning across expansive codebases
For instance, the Scout model can be used to analyze all documents in a company’s SharePoint library to respond to specific inquiries or to review a lengthy technical manual for troubleshooting guidance. Essentially, it serves as a diligent scout, sifting through extensive information to deliver precise insights.
Llama 4 Maverick: Innovation at Scale
The Llama 4 Maverick model functions as a general-purpose large language model (LLM), incorporating:
- 17 billion active parameters
- 128 experts
- 400 billion total parameters
This model is designed for high efficiency and is offered at a more competitive price when compared to earlier models.
Maverick excels particularly in tasks that require both text and image comprehension. It supports 12 languages, making it a valuable asset for developing innovative AI applications that can seamlessly navigate language barriers. Some targeted applications for Maverick include:
- Customer support bots that interpret uploaded images
- AI partners that can create and discuss content across multiple languages
- Internal assistants within organizations that can clarify user queries and process rich media inputs
Architectural Innovations in Llama 4
Llama 4 features two primary advancements that enhance its capabilities:
Early-Fusion Multimodal Transformer
This model uses an early-fusion strategy where text, images, and videos are processed as a single sequence from the onset. This design allows the model to generate and comprehend various media types together, ideal for tasks such as:
- Analyzing documents containing diagrams
- Responding to queries about a video that combines visuals and transcripts
Mixture of Experts (MoE) Architecture
The Llama 4 model incorporates a sparse Mixture of Experts architecture that enhances performance without excessive computational costs. This system consists of many expert models, with only a few being utilized for any specific input. This design not only streamlines the training process but also enhances the scalability of the model by allowing it to handle multiple queries concurrently.
Commitment to Safety and Best Practices
Meta has prioritized safety in the development of Llama 4 by employing best practices to safeguard against potential risks. Throughout the development phases, they include various mitigations to protect against adversarial attacks. By utilizing Azure AI Foundry, developers benefit from robust safety and security measures.
Developers can now explore the Llama 4 models available in the Azure AI Foundry Model Catalog or Azure Databricks. This offering equips them with the latest advancements in multimodal, MoE-powered AI, supported by both Meta’s research and Azure’s capabilities. The integration of these models enables organizations to unlock sophisticated and secure AI solutions tailored to their specific needs.
