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Reflection Beam brings open weights and long-horizon coding to AWS

aws.amazon.com by Clement Perrot 9 min read Editor score 7/10

The guide covers deploying Beam through SageMaker and HyperPod with SGLang or vLLM, and explains how its 501B total and 23B active parameters affect memory and parallelism. It connects agentic coding workloads to practical serving choices.

What: Reflection Beam is an open-weight model for coding and long-horizon tasks, with 501B total and 23B active parameters.
How: Beam weights are available in BF16, FP8, and NVFP4; AWS offers managed JumpStart deployment or configurable HyperPod serving.
Why it matters: Engineers can choose managed endpoints or control runtime and distributed execution, while sizing for memory, context, concurrency, and latency.

The managed JumpStart path deploys Beam behind an endpoint and invocation API. Sketched from the article's own text.

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