The Oracle Cloud Infrastructure (OCI) Generative AI service has welcomed an exciting new addition: the Meta Llama 4 model family, featuring Scout and Maverick. These models incorporate a unique Mixture of Experts (MoE) architecture, significantly enhancing processing power and efficiency. They are specifically optimized to excel in various domains, including multimodal understanding, multilingual tasks, code generation, and tool calling, and are capable of powering advanced agent systems.
Currently, these models are available in generally available (GA) versions in the following regions:
- On-demand: ORD (Chicago)
- Dedicated AI Clusters: ORD (Chicago), GRU (Guarulhos), LHR (London), KIK (Kikuyu)
Key Highlights of the Llama 4 Family
Multimodal Capabilities: Breaking the Boundaries of Data Types
Llama 4 Scout and Maverick are not merely language models; they are true multimodal masters. They can natively process and integrate various data types, including text and images, enabling richer and more comprehensive AI applications. Imagine an AI system that can simultaneously understand a textual description and a related image, allowing it to better grasp the context and make more informed decisions. This multimodal capability opens up new possibilities for tasks such as image captioning and visual question answering.
The ability to handle both text and image inputs allows Llama 4 to perform tasks that were previously difficult or impossible for traditional language models. For instance, in the context of e-commerce, a user could provide a product image and ask the AI to generate a descriptive caption for the product listing. Alternatively, in a customer support scenario, an agent could use both a customer’s textual query and a related image to diagnose a problem more accurately and provide a more helpful response. The potential applications are vast and extend across numerous industries.
Furthermore, the multimodal capabilities of Llama 4 facilitate the development of more intuitive and user-friendly AI systems. Users can interact with the AI using a combination of text and images, making the interaction more natural and expressive. This is particularly important for applications that involve visual content, such as image editing, design, and entertainment.
The integration of multimodal capabilities into Llama 4 represents a significant step forward in the development of more versatile and powerful AI systems. As AI continues to evolve, the ability to seamlessly process and integrate different data types will become increasingly important for unlocking its full potential.
Multilingual Support: Communication Without Borders
Another major highlight of the Llama 4 series is its robust multilingual support. These models were trained on datasets containing 200 languages and fine-tuned for 12 major languages (Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese). This means they can understand and generate text in multiple languages, opening the door to global applications. It is worth noting that image understanding functionality currently only supports English.
The extensive multilingual support of Llama 4 makes it an ideal choice for companies operating in international markets or serving diverse customer bases. Businesses can leverage Llama 4 to automate tasks such as translation, content creation, and customer support in multiple languages, thereby reducing costs and improving efficiency.
Moreover, the multilingual capabilities of Llama 4 can help to bridge the language barrier and facilitate communication between people from different cultural backgrounds. This is particularly important in areas such as education, healthcare, and international relations. By enabling AI systems to understand and generate text in multiple languages, Llama 4 can contribute to a more connected and inclusive world.
The fine-tuning of Llama 4 on 12 major languages ensures that it performs exceptionally well in these languages, providing accurate and fluent translations and content generation. The model’s ability to handle a wide range of languages makes it a valuable tool for organizations that need to communicate effectively with a global audience.
Efficient Development: Smaller GPU Footprint
For developers, Llama 4 Scout was designed with accessibility in mind. It can run efficiently on a smaller GPU footprint, making it an ideal choice for resource-constrained environments. This means that even without powerful hardware, developers can leverage Llama 4 Scout’s powerful features to accelerate the development and deployment of AI applications.
The smaller GPU footprint of Llama 4 Scout makes it accessible to a wider range of developers, including those who may not have access to expensive or high-performance hardware. This democratization of AI development is crucial for fostering innovation and ensuring that the benefits of AI are shared by all.
Furthermore, the efficient design of Llama 4 Scout allows it to be deployed on edge devices, such as smartphones and embedded systems. This opens up new possibilities for AI applications that can run directly on devices, without the need for a constant connection to the cloud. For example, Llama 4 Scout could be used to power a real-time translation app on a smartphone or to enable intelligent automation in a smart home.
The reduced computational requirements of Llama 4 Scout also contribute to its lower energy consumption, making it a more sustainable choice for AI development. As concerns about the environmental impact of AI continue to grow, the efficient design of Llama 4 Scout is a welcome development.
Open-Source Models: Empowering the Community
Meta has chosen an open approach, releasing these two models under the Llama 4 Community License. This means that developers are free to fine-tune and deploy them, subject to specific license terms. This open model promotes innovation and collaboration within the AI community, allowing more people to participate in the development and application of AI technology.
The open-source nature of Llama 4 encourages developers to experiment with the models, adapt them to their specific needs, and contribute their improvements back to the community. This collaborative approach can lead to faster innovation and the development of more robust and versatile AI systems.
Moreover, the Llama 4 Community License allows developers to use the models for both research and commercial purposes, providing a flexible and permissive framework for their application. This broad accessibility is crucial for fostering the widespread adoption of AI technology and promoting its use in a variety of industries and applications.
The open-source model also provides transparency into the inner workings of the Llama 4 models, allowing researchers and developers to better understand their behavior and identify potential biases or limitations. This transparency is essential for building trust in AI systems and ensuring that they are used responsibly.
Knowledge Cutoff Date
It is important to note that the knowledge cutoff date for the Llama 4 models is August 2024. This means they may not be able to provide up-to-date information on events or information that occurred after this date.
The knowledge cutoff date is a common limitation of large language models, as they are trained on a fixed dataset of information. While the models can continue to learn and adapt over time, they will not be able to access or process information that was not included in their training data.
Therefore, it is important to be aware of the knowledge cutoff date when using Llama 4 models and to supplement their output with other sources of information when necessary. For example, if you are using Llama 4 to research a current event, you should also consult news articles, official reports, and other reliable sources to ensure that you have the most up-to-date information.
Despite this limitation, Llama 4 models can still be a valuable tool for accessing and processing information, as they have a vast amount of knowledge about a wide range of topics. However, it is important to use them judiciously and to be aware of their limitations.
Important Note: The Llama acceptable use policy restricts its usage within the European Union (EU).
This restriction is likely due to regulatory concerns related to data privacy and artificial intelligence in the EU. The EU has implemented strict regulations, such as the General Data Protection Regulation (GDPR), which govern the collection, processing, and use of data within the region.
Meta may have chosen to restrict the use of Llama models in the EU to ensure compliance with these regulations or to avoid potential legal challenges. It is important for users in the EU to be aware of this restriction and to comply with the Llama acceptable use policy.
Llama 4 Scout: Lightweight Champion
Architecture: Ingenious Parameter Design
Llama 4 Scout employs a clever architecture design, activating only 17 billion parameters out of a total of approximately 109 billion parameters. This design leverages a mixture of 16 experts, striking a good balance between performance and efficiency. By activating only a subset of parameters, Scout significantly reduces computational requirements, allowing it to run in resource-constrained environments.
The Mixture of Experts (MoE) architecture is a key innovation that enables Llama 4 Scout to achieve high performance with a relatively small number of active parameters. In a MoE model, the parameters are divided into multiple “experts,” each of which is specialized in a particular area of expertise. When processing an input, the model selects a subset of the experts to activate based on the characteristics of the input. This allows the model to focus its computational resources on the most relevant experts, thereby improving efficiency and performance.
The choice of 16 experts in Llama 4 Scout represents a trade-off between the ability to specialize and the overhead of managing multiple experts. A larger number of experts would allow for greater specialization, but it would also increase the complexity of the model and the computational cost of selecting the appropriate experts for each input.
The ingenious parameter design of Llama 4 Scout makes it an attractive option for developers who need to deploy AI models in resource-constrained environments, such as mobile devices, embedded systems, and edge computing platforms.
Context Window: Ability to Process Long Text
Llama 4 Scout supports a context length of up to 10 million tokens (requiring multiple GPUs). However, at general availability (GA), the OCI Generative AI service will support a context length of 192k tokens. Even a context window of 192k is sufficient to process fairly long texts, such as book chapters or detailed reports.
The context window refers to the amount of text that the model can consider when generating a response. A larger context window allows the model to capture more context and generate more coherent and relevant responses.
The ability of Llama 4 Scout to support a context length of up to 10 million tokens is a significant achievement, as it allows the model to process extremely long and complex texts. However, the computational requirements for processing such long texts are substantial, requiring multiple GPUs.
The OCI Generative AI service will initially support a context length of 192k tokens for Llama 4 Scout, which is still sufficient for processing many real-world texts. As hardware and software technology continue to improve, it is likely that the supported context length will increase over time.
The large context window of Llama 4 Scout makes it well-suited for tasks such as summarization, question answering, and content generation, where the ability to capture long-range dependencies and context is crucial.
Deployment: Compact and Powerful
One of the design goals of Llama 4 Scout was to run efficiently on a smaller GPU footprint. This makes it an ideal choice for various deployment scenarios, including edge devices and resource-constrained cloud environments.
The compact size and efficient performance of Llama 4 Scout make it a versatile model that can be deployed in a wide range of environments. It can be deployed on edge devices, such as smartphones and embedded systems, to enable real-time AI applications without the need for a constant connection to the cloud.
It can also be deployed in resource-constrained cloud environments, where the cost of GPU resources is a significant factor. The smaller GPU footprint of Llama 4 Scout allows developers to reduce their costs and deploy AI applications more efficiently.
The deployment flexibility of Llama 4 Scout makes it an attractive option for developers who need to deploy AI models in diverse environments and meet varying performance and cost requirements.
Performance: Outperforming Competitors
Llama 4 Scout has demonstrated excellent performance in several benchmark tests, outperforming models such as Google’s Gemma 3 and Mistral 3.1. This demonstrates Scout’s superior capabilities in terms of performance, making it a powerful tool for various AI tasks.
The benchmark tests provide objective measures of the performance of Llama 4 Scout on a variety of AI tasks, such as natural language understanding, text generation, and question answering. The fact that Llama 4 Scout outperforms competitors such as Google’s Gemma 3 and Mistral 3.1 is a testament to its superior capabilities and efficient design.
The excellent performance of Llama 4 Scout makes it a compelling option for developers who are looking for a high-performance AI model that can deliver accurate and reliable results. It can be used for a wide range of AI tasks, from simple sentiment analysis to complex natural language processing tasks.
Llama 4 Maverick: Heavyweight Contender
Architecture: Greater Scale, Greater Power
Compared to Scout, Llama 4 Maverick adopts a larger architecture scale. It also activates 17 billion parameters, but it does so within a larger framework of approximately 400 billion parameters, utilizing 128 experts. This larger scale gives Maverick greater power, enabling it to excel in more complex AI tasks.
The larger architecture scale of Llama 4 Maverick, with its 400 billion parameters and 128 experts, allows it to capture more complex patterns and relationships in data. This enables it to perform better on challenging AI tasks that require a deep understanding of language and context.
The choice of 128 experts in Llama 4 Maverick allows for greater specialization and better adaptation to different types of inputs. This makes it particularly well-suited for tasks that require a broad range of expertise, such as complex question answering and creative content generation.
The greater scale and power of Llama 4 Maverick make it an ideal choice for developers who need to tackle challenging AI tasks and achieve state-of-the-art results.
Context Window: Super Long Memory
Llama 4 Maverick supports a context length of up to 1 million tokens. At general availability (GA), the OCI deployment will support a context length of 512k tokens. Such a long context window allows Maverick to process extremely complex texts, such as entire books or collections of multiple documents.
The ability of Llama 4 Maverick to support a context length of up to 1 million tokens is a remarkable achievement, as it allows the model to process extremely long and complex texts without losing context or coherence. This is particularly important for tasks that require a deep understanding of narrative structure, argumentative reasoning, or logical inference.
The OCI deployment will initially support a context length of 512k tokens for Llama 4 Maverick, which is still sufficient for processing many real-world texts, including entire books and collections of documents.
The super-long memory of Llama 4 Maverick makes it well-suited for tasks such as literature analysis, legal research, and scientific discovery, where the ability to process and understand large amounts of text is crucial.
Deployment: Needs More Space
Due to its larger size, Llama 4 Maverick requires more deployment space than Scout. At GA, the Maverick deployment on OCI requires approximately twice the space of Scout.
The larger size of Llama 4 Maverick is a trade-off for its greater power and performance. Developers need to consider the deployment space requirements when choosing between Llama 4 Scout and Llama 4 Maverick.
If deployment space is a significant constraint, Llama 4 Scout may be a better choice. However, if the goal is to achieve the highest possible performance, Llama 4 Maverick is the preferred option.
Performance: Comparable to Top Models
In code generation and reasoning tasks, Llama 4 Maverick’s performance is comparable to top models such as OpenAI’s GPT-4o and DeepSeek-V3. This demonstrates Maverick’s leading position in the field of AI.
The ability of Llama 4 Maverick to achieve performance comparable to top models such as OpenAI’s GPT-4o and DeepSeek-V3 is a significant accomplishment. This demonstrates that Llama 4 Maverick is a state-of-the-art AI model that can deliver excellent results on a wide range of tasks.
The excellent performance of Llama 4 Maverick in code generation and reasoning tasks makes it a valuable tool for developers who are building AI-powered applications that require these capabilities. It can be used to automate tasks such as code completion, bug detection, and software testing.
In conclusion, the Llama 4 family represents a significant advancement in AI model development. They offer significant improvements in performance, versatility, and accessibility, providing powerful support for a wide range of application scenarios.
OCI customers can now easily leverage these powerful models without worrying about the complexities of infrastructure management. They can access these models through a chat interface, API, or dedicated endpoint, streamlining the development and deployment of AI applications.
The release of the Llama 4 models marks a new era for the OCI Generative AI service. By providing these advanced models, OCI is helping customers unlock the full potential of AI and drive innovation across various industries.