AWS outlined deployment options for Reflection AI’s open-weight Beam model, including managed SageMaker endpoints and Kubernetes-based inference across supported regions.
AWS published deployment guidance for Reflection AI’s Beam on October 16, 2026, describing ways to run the model through Amazon SageMaker. Beam is designed for coding agents and multi-step tasks that can involve changing code, using tools, and acting on intermediate results. The post presents AWS hosting options; it does not report benchmark results for the model.
Beam is a sparse mixture-of-experts model with 501 billion total parameters and 23 billion active parameters. Reflection reports an effective context length of 1 million tokens. Its weights are distributed through Hugging Face under the Apache 2.0 license, with Beam 1.0 checkpoints in BF16, FP8, and NVFP4 formats.
Developers seeking a managed route can use a SageMaker JumpStart real-time endpoint. For greater infrastructure control, AWS describes running the model on SageMaker HyperPod Inference, either with the managed JumpStart configuration or with SGLang or vLLM. HyperPod’s Kubernetes-based setup supports control over distributed execution, caching, routing, and scaling; the custom serving runtimes offer additional configuration choices.
The model’s size makes hardware planning central to deployment. AWS advises accounting for checkpoint storage, GPU memory, the key-value cache, runtime overhead, and expected workload. Its guidance names H100-based instances for FP8, H200-based instances for FP8, and B200-based instances for NVFP4, while noting that users need to confirm compatibility among the checkpoint, runtime, and parallel layout.
At the time of the post, Beam was available through SageMaker JumpStart in the US East (N. Virginia) and US East (Ohio) regions. The practical distinction is between a more managed endpoint and a cluster-oriented deployment where teams can tune the serving stack to their requirements.