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Reflection AI introduced Beam, an open-weight model for agentic coding and long-horizon tasks, deployable on AWS through SageMaker JumpStart or SageMaker HyperPod Inference with SGLang or vLLM.

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Reflection Beam: An Open-Weight Model for Agentic Coding on AWS

What was announced?

FAQ

What is Reflection Beam?

It is an open-weight model from Reflection AI designed for agentic coding and long-horizon tasks, meaning multi-step work that involves sequencing tools and actions.

How can Beam be deployed on AWS?

You can start with an Amazon SageMaker JumpStart real-time endpoint, or use Amazon SageMaker HyperPod Inference with JumpStart, SGLang, or vLLM for Kubernetes-native control over distributed GPU infrastructure.

Is Beam relevant to AI teams in the Middle East?

Yes. As an open-weight model deployable on SageMaker, it gives teams more control over data and infrastructure, which matters for government and enterprise adopters in the region.

What is the difference between SageMaker JumpStart and HyperPod Inference for Beam?

JumpStart offers fast deployment through a real-time endpoint, while HyperPod Inference provides Kubernetes-native control over distributed GPU infrastructure, suited to larger workloads.

Source: AWS Machine Learning

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