Introducing Reflection Beam: an open-weight model for agentic coding on AWS

Reflection AI introduced Beam, an open-weight model for agentic coding and long-horizon tasks such as maintaining code, using tools, interpreting results, and completing multi-step workflows. The open-weight release gives developers and researchers control over the runtime, deployment environment, and customization process. In this post, we explain Reflection’s vision and show how you can deploy Beam on AWS. Start with an Amazon SageMaker JumpStart real-time endpoint for a fully managed path. When you need Kubernetes-native control over distributed GPU infrastructure, use Amazon SageMaker HyperPod Inference with JumpStart, SGLang, or vLLM. We also explain how Beam’s 501 billion total parameters shape checkpoint storage, weight memory, sharding, and parallelism, while its 23 billion active parameters influence per-token compute. At launch, Beam is available in Amazon SageMaker JumpStart in US East (N. Virginia) and US East (Ohio). Reflection’s vision for open intelligence Reflection’s mission is to make intelligence open and accessible to all through open model weights, published research, and open source software. Its full AI stack spans models, core software, production infrastructure, and applications. Beam brings this approach to developers building software engineering and agentic systems. About Reflection Beam Beam is a sparse mixture-of-experts model for resolving software issues, modifying code, calling tools, and acting on intermediate results. It has 501 billion total parameters and 23 billion active parameters, and Reflection reports an effective context length of 1 million tokens. Reflection releases Beam’s weights through Hugging Face under the Apache 2.0 license. Beam 1.0 is available in BF16, FP8, and NVFP4 checkpoint variants. These formats provide different memory footprints and hardware compatibility. You can deploy the model with the AWS managed JumpStart container configuration or inspect and customize the model with SGLang or vLLM in your AWS environment. Understand the deployment architecture Choose the serving path that meets your control and scaling requirements. Start with a SageMaker JumpStart real-time endpoint when you want an AWS managed model package, endpoint, and invocation API without operating Kubernetes. Use JumpStart on HyperPod Inference when you want the AWS managed container and model configuration on a HyperPod cluster orchestrated by Amazon Elastic Kubernetes Service (Amazon EKS). Use SGLang or vLLM on HyperPod Inference when you need to control the serving image, runtime arguments, quantization, or distributed execution. Size the deployment for checkpoint storage, weight memory, KV cache, context length, batching, runtime overhead, and the supported parallelism. Define the expected concurrency and latency targets, then configure replicas, caching, routing, and autoscaling from measured workload behavior. The JumpStart real-time path packages the model, container, and endpoint behind a managed deployment workflow. The HyperPod path exposes the cluster and serving runtime so that you can control distributed execution, caching, routing, and scaling. The following diagram shows the components used by the HyperPod path. Beam can be sourced through SageMaker JumpStart, Hugging Face Hub, or an optional Amazon Simple Storage Service (Amazon S3) mirror. The distributed workers use the AWS managed JumpStart container or a custom SGLang or vLLM runtime. Figure 1: Distributed Beam deployment with Amazon SageMaker HyperPod Inference Prerequisites You need: An AWS account with permissions for your selected SageMaker deployment path and its supporting AWS Identity and Access Management (IAM), networking, and storage resources. Access to Beam through SageMaker JumpStart or one of the Beam 1.0 checkpoint repositories on Hugging Face. Acceptance of the model terms, the AWS Command Line Interface (AWS CLI), and Python 3.11 or later. GPU quota and capacity for a supported Beam configuration. Plan and obtain GPU capacity Beam’s 501 billion total parameters require a multi-GPU deployment. BF16 has the largest weight-memory requirement, so plan a multi-node configuration with enough aggregate GPU memory for the weights and runtime overhead. For FP8, use ml.p5.48xlarge with NVIDIA H100 GPUs or ml.p5e.48xlarge and ml.p5en.48xlarge with H200 GPUs. For NVFP4, use ml.p6-b200.48xlarge with NVIDIA B200 GPUs. Confirm that the released checkpoint, runtime, and parallel layout support the selected instance type. Calculate memory for weights, KV cache, and runtime overhead for one replica, then confirm the estimate with a load test. Choose an AWS Region that supports the required accelerated instance. Check the service quota and available capacity for the selected instance type. Use on-demand capacity when available, or use a SageMaker training plan as a short-term reservation for scheduled work. Allow time for image transfer, weight download, shard loading, and runtime initialization. Path 1: Get started with a SageMaker JumpStart real-time endpoint Use a JumpStart real-time endpoint first when you want the shortest path from the model catalog to a managed HTTPS endpoint. The sample supports the BF16, FP8, and NVFP4 model IDs through one variant-aware command. Its README and Python file are the source of truth for supported instance types, request schema, deployment command, and cleanup command. Clone the sample repository. git clone https://github.com/aws-samples/sagemaker-genai-hosting-examples.git Enter the SageMaker JumpStart deployment sample directory. cd sagemaker-genai-hosting-examples/01-models/ReflectionAI/Beam-1.0-501B-A23B-NVFP4/ Create and activate a Python virtual environment. python3 -m venv .venv source .venv/bin/activate Install the sample dependency. python -m pip install -r requirements.txt Run the bounded deployment, invocation, and cleanup lifecycle for FP8. python jumpstart_real_time.py \ --variant fp8 \ --endpoint-name reflection-beam-fp8-smoke The command deploys the FP8 endpoint, sends a short request, prints the response, and cleans up the endpoint. Use bf16 or nvfp4 to select another checkpoint variant; NVFP4 is the default when you omit --variant . Use the Python functions in the README when you need to keep an endpoint running. Path 2: Deploy with SageMaker HyperPod Inference Use HyperPod Inference when you need Kubernetes-native control over distributed GPU infrastructure. You can use the AWS managed JumpStart container configuration or deploy Beam with SGLang or vLLM. The HyperPod Inference deployment sample README and its configuration files are the source of truth for released manifests, model and image identifiers, supported runtime versions, and commands. Before you begin, configure kubectl access to an EKS-orchestrated HyperPod cluster with the HyperPod Inference Operator and required add-ons. You also need the cluster virtual private cloud (VPC), subnets, security groups, access to the selected Beam and runtime artifacts, and local NVMe capacity if you enable model caching. Use an isolated Hugging Face access token if the selected repository requires authentication. Deploy a pinned Beam revision from Hugging Face Hub for development and experimentation. For production deployments and model caching, copy the checkpoint to Amazon S3 or Amazon FSx. HyperPod model and image caching can preload weights onto node-local NVMe storage and pre-pull the serving image. The HyperPod Inference Gateway provides an OpenAI-compatible interface and routes requests across replicas. HyperPod Inference also supports managed tiered KV caching and intelligent routing . These are among the Amazon SageMaker Inference capabilities introduced in 2026 . The next diagram expands the model preparation and runtime request flow. When model caching is enabled, the Inference Operator warms eligible nodes with the selected weights and serving image. At runtime, the Inference Gateway and Endpoint Picker can direct each request to a suitabl