Introducing Reflection Beam: an open-weight model for agentic coding on AWS
Amazon Web Services Clement Perrot ● Covered by 3 sources
Reflection AI launched Beam, an open-weight sparse Mixture-of-Experts model for agentic coding and long-horizon software tasks. Beam has 501 billion total parameters, with 23 billion active parameters per forward pass. The release adds new options to deploy Beam on AWS—using SageMaker JumpStart or Kubernetes-native SageMaker HyperPod Inference—and to choose BF16, FP8, or NVFP4 checkpoint formats with corresponding memory and scaling tradeoffs.
Why it matters
Reflection AI's Beam is an open-weight model for agentic coding and long-horizon tasks. This post shows how to deploy Beam on AWS: 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.
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