Beam model by Reflection is now available on Amazon SageMaker JumpStart and AWS Trainium

Beam, the first open weight model offered by US-based Reflection, is now available in Amazon SageMaker JumpStart, the model hub in Amazon SageMaker AI. According to Beam, they operate at the frontier of open weight models in its class, with strong performance across reasoning, coding, and agentic benchmarks. Beam is for teams who build on and with an open weight model, not just call one. Customers who want to adapt a frontier model to their own domain can fine-tune it, inspect and modify its weights, and embed it deep in their agents and workflows, all while keeping full control of their data and infrastructure. Beam runs alongside other models that customers already use in SageMaker AI and scales to meet production demand with the same controls, audit trails, and monitoring built into every SageMaker AI deployment. Additionally, Beam was ported and optimized for AWS Trainium3, giving customers a high-performance, cost-efficient option for deploying and scaling the model from day one. “Reflection is delivering frontier AI that developers can actually control, not just rent through an API. Bringing Beam to SageMaker AI puts that capability in front of millions of developers on infrastructure they already trust, running in their own AWS account, on their own data, fully in their control," said Misha Laskin, co-founder and CEO, Reflection.

Teams building on Beam can take it well beyond calling an API: fine-tune it on proprietary code and data, specialize it for their own domains, and wire it into multi-step agentic workflows across their tools. In practice that means resolving real software issues, generating and reviewing code, automating operations work, and tackling advanced math and science reasoning on a model they can shape rather than one they simply consume. Because Beam is released under the Apache 2.0 license, customers can inspect and customize the model, while also running it in their own AWS accounts, so data stays private and under their control, protected by the same AWS security and compliance capabilities they already rely on. “Advanced AI models like Beam, now available on Amazon SageMaker, let us run open weight frontier AI entirely inside our own secure environments, with full visibility into the weights, the data, and the pipeline. In the age of multi-model AI, the pairing of openness and managed infrastructure is an essential addition to the AI ecosystem. The freedom to deploy and adapt advanced AI, tailored to mission problems with client-specific security and controls, gives more agencies greater flexibility to deploy advanced AI across their enterprise,” said Garrett Berntsen, Chief AI Officer, Accenture Federal Services.

Beam is initially available in Amazon SageMaker JumpStart in US East (N. Virginia) and US East (Ohio). The model is available in BF16, FP8, and NVFP4 checkpoint variants, and can be deployed through a fully managed SageMaker JumpStart endpoint. To start, search for "Beam" in SageMaker JumpStart within Amazon SageMaker Studio, or deploy it directly on Sagemaker AI. To learn more, read the AWS Machine Learning blog: Introducing Reflection Beam, SageMaker AI documentation and SageMaker JumpStart documentation for deployment guidance.