Reflection Beam reaches AWS through managed and configurable deployment
AWS has added Reflection Beam to SageMaker. The launch outlines managed JumpStart and configurable HyperPod routes; separate first-party documents clarify checkpoints, language scope, interfaces, and metrics.
What AWS added
The AWS announcement says Beam is available in SageMaker JumpStart in US East (N. Virginia) and US East (Ohio). It presents a managed real-time endpoint as the simpler route, while HyperPod Inference offers Kubernetes-level control with JumpStart, SGLang, or vLLM.
Reflection’s release note says the company released the weights on October 16 under Apache 2.0. Reflection describes Beam as a sparse mixture-of-experts model with 501 billion total parameters and 23 billion active parameters, aimed at coding, reasoning, and long-horizon agentic work. These are provider statements, not independent performance findings.
What a developer can check
The model card describes this instruction-tuned release as text-in and text-out, with BF16, FP8, and NVFP4 checkpoints, and says it was developed predominantly for English. A team therefore needs to select the exact checkpoint and evaluate language fit on its own workload rather than assume identical behavior across variants or languages.
AWS’s HyperPod documentation separates JumpStart open-weight or gated models from custom models stored in S3 or FSx, and supports single- or multi-node inference. Its observability overview names time to first token, latency, and GPU utilization, giving developers concrete deployment signals to record instead of treating a successful launch as a complete evaluation.
The separate deployment overview lists Studio, kubectl, the Python SDK, and the HyperPod CLI, but the available route depends on where the model comes from. That distinction matters when reproducing a test: the interface, model source, checkpoint, and infrastructure path should remain explicit in the test record.