Introducing Reflection Beam: an open-weight model for agentic coding on AWS | Amazon Web Services
Reflection AI introduced Beam, an open-weight model for agentic coding and long-horizon tasks such as maintaining code, using tools, interpreting results, and completing multi-step workflows. The open-weight release gives developers and researchers control over the runtime, deployment environment, and customization process.
In this post, we explain Reflection’s vision and show how you can deploy Beam on AWS. Start with an Amazon SageMaker JumpStart real-time endpoint for a fully managed path. When you need Kubernetes-native control over distributed GPU infrastructure, use Amazon SageMaker HyperPod Inference with JumpStart, SGLang, or vLLM. We also explain how Beam’s 501 billion total parameters shape checkpoint storage, weight memory, sharding, and parallelism, while its 23 billion active parameters influence per-token compute.
At launch, Beam is available in Amazon SageMaker JumpStart in US East (N. Virginia) and US East (Ohio).
Reflection’s vision for open intelligence
Reflection’s mission is to make intelligence open and accessible to allthrough...
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