Meta’s newest AI models, Llama 4 Scout and Llama 4 Maverick, are now available on Amazon SageMaker JumpStart, with serverless access via Amazon Bedrock coming soon. These advanced models combine powerful image and text understanding with efficient performance to support next-gen AI applications.
Amazon Web Services (AWS) has announced the availability of Meta’s Llama 4 AI models through Amazon SageMaker JumpStart, a platform that helps users build and deploy machine learning models quickly. This move marks a major step in making multimodal AI—which processes both images and text—easier to use and more cost-effective for businesses and developers. A serverless option in Amazon Bedrock is expected to launch soon.
What Makes Llama 4 Models Unique?
Built for Both Text and Images
Unlike older AI models, Llama 4 was designed from the start to handle both images and text. This native multimodality allows users to create more advanced tools, like chatbots that can understand photos or systems that can summarize documents with images.
Smarter Use of Computing Power
Llama 4 uses a smart architecture called Mixture of Experts (MoE). Instead of using the entire model for every task, it only activates the parts it needs—just like how a hospital sends patients to the right specialist. This makes Llama 4 more efficient, saving both energy and money while still delivering powerful results.
“This release brings more choice and flexibility to developers,” AWS said in its official statement. “The combination of Meta’s innovation and AWS infrastructure enables rapid deployment of cutting-edge AI tools.”
Meet Llama 4 Scout and Maverick
Llama 4 Scout 17B
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17 billion active parameters
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109 billion total parameters
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Context window: up to 10 million tokens
Scout is ideal for tasks that require deep focus and long memory. It can process up to 10 million tokens at once—about 80 times more than the Llama 3 model. This allows it to summarize multiple documents, understand complex user activity, or analyze large codebases all at once.
Llama 4 Maverick 17B
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17 billion active parameters
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400 billion total parameters
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Uses 128 expert modules, activating only what’s needed
Maverick is a general-purpose model known for its skill in both language and visual tasks. It understands 12 languages and is well-suited for applications like intelligent assistants and image analysis. It combines high performance with efficient computing, making it ideal for cost-sensitive projects.
What’s the Impact?
This launch boosts AWS’s ongoing strategy to offer more generative AI models from top providers like Meta. With Llama 4 now in the mix, developers have access to faster, smarter, and more affordable tools for building new applications.
More than 135 free and low-cost AWS courses are also available to help developers get started with these tools, covering both beginner and expert topics in AI and machine learning.
Coming Soon: Serverless Llama 4 Models on Amazon Bedrock
AWS plans to release fully managed, serverless versions of Llama 4 via Amazon Bedrock. This will let developers run these models without needing to manage infrastructure, making it even easier to launch scalable AI tools.
“We’re just scratching the surface,” said an AWS spokesperson. “Customers can expect ongoing updates, including new model sizes and features, in the near future.”
Why This Matters for Businesses
The Llama 4 models make advanced AI more accessible. Their efficient design helps cut costs, while their deep capabilities open doors for applications in:
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Healthcare (image and document processing)
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Finance (summarizing reports, fraud detection)
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Retail (personalized assistants, visual search)
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Education (AI tutors, content generation)
Companies no longer need massive budgets or expert teams to access top-tier AI. With tools like Scout and Maverick, powerful AI can now run faster, cheaper, and smarter.
The Bigger Picture
Meta’s Llama 4 release reflects a shift in how AI is built and delivered. The MoE architecture shows how AI can now be both powerful and efficient, and its multimodal capabilities point toward a future where AI tools can see, read, and understand complex information more like humans.
