Amazon Web Services (AWS) has announced the availability of Meta’s new Llama 4 models via Amazon SageMaker JumpStart, with availability as fully managed, serverless models in Amazon Bedrock coming soon. The first two models in the Llama 4 herd—Llama 4 Scout 17B and Llama 4 Maverick 17B—both feature advanced multimodal capabilities (the ability to understand both image and text prompts) and industry-leading context windows (how much information they can process at once) for improved performance and efficiency over previous model versions.
The availability of Llama 4 Scout and Llama 4 Maverick on AWS expand the already broad selection of models offered to customers to build, deploy, and scale their applications. AWS consistently offers new models from leading AI companies such as Meta as soon as the models are released, with enterprise-grade tools and security that make it easy to build, customize, and scale generative AI applications.
Today’s news further reinforces AWS’s commitment to model choice with two new advanced multimodal models from Meta. Llama 4 Scout 17B significantly expands what AI can process at once—from 128,000 tokens in previous Llama models to now up to 10 million tokens (nearly 80x the previous context length)—underpinning applications that can summarize multiple documents together, analyze comprehensive user activity patterns, or reason through entire code bases at once. Llama 4 Maverick 17B is a general-purpose model that excels in image and text understanding tasks across 12 languages, making it well suited for sophisticated assistants and chat applications.
Both Llama 4 models are built with native multimodality, meaning they’re designed from the ground up to seamlessly understand text and images together, rather than handling them as separate inputs. And thanks to their more efficient mixture of experts (MoE) architecture—a first for Meta—that activates only the most relevant parts of the model for each task, customers can benefit from these powerful capabilities that are more compute efficient for model training and inference, translating into lower costs at greater performance.
SOURCE: Aboutamazon
