Skip to content
K = 0.731

Reflection Releases Beam Coding Model as AWS Details SageMaker Deployment

1 reports 1 reporting sources 14 hours agoNew

Understand the event

AI overview

Reflection AI introduced Beam 1.0, an open-weight model for agentic coding and multi-step tasks, and AWS published instructions for deploying it through Amazon SageMaker JumpStart or HyperPod Inference. At launch, Beam is available in JumpStart in US East (N. Virginia) and US East (Ohio). Beam has 501 billion total parameters and 23 billion active parameters. Reflection reports an effective context length of 1 million tokens and released the model under Apache 2.0 in BF16, FP8, and NVFP4 variants. AWS’s guide covers capacity planning, invocation, performance measurement, and cleanup, with deployment options using JumpStart, SGLang, or vLLM. For FP8 deployment, AWS specifies ml.p5.48xlarge instances with NVIDIA H100 GPUs or ml.p5e.48xlarge and ml.p5en.48xlarge instances with H200 GPUs; for NVFP4, it specifies ml.p6-b200.48xlarge instances with B200 GPUs.

Generated by AI from reports · Updated 13 hours ago

Latest development10/16 23:23 (UTC+8)
Introducing Reflection Beam, an open-weight model for agentic coding on AWSAWS Machine Learning Blog

Report timeline

Follow the reports to explore different sides of the event.

10/16
  1. AWS Machine Learning BlogPicks
    Introducing Reflection Beam, an open-weight model for agentic coding on AWS

    Reflection AI introduced Beam, an open-weight model for agentic coding and multi-step tasks, with 501 billion total parameters and 23 billion active parameters. Reflection reports an effective context length of 1 million tokens and releases Beam 1.0 under Apache 2.0 in BF16, FP8, and NVFP4 variants. The guide explains deployment through Amazon SageMaker JumpStart or HyperPod Inference with JumpStart, SGLang, or vLLM, including capacity planning, invocation, performance measurement, and cleanup.

Event heat trend

可比范围当前
7
Peak within comparable coverage
1010/17 00:00 (UTC+8)
近 24 小时变化
–

Trends compare the same continuously observed participants, which may cover fewer sources than current heat. Point or click to inspect hourly heat, or use left and right arrow keys.

Keep reading

Latest daily · Issue 24NVIDIA and AI4Bharat Introduce Nemotron-pretraining-Indic-v1 and an Open Data Generation Pipeline

Trending now

  1. 1Meta Releases Muse Spark Open With Mac and Cloud Support, Topping US Open-Weight Index
  2. 2Google Launches Gemini 4 Argon Globally, Expands Paid Access as Benchmark Results Vary
  3. 3OpenAI Updates GPT-6 Astra Pro in ChatGPT With Parallel Agents and Faster Responses
  4. 4Meta’s New Business Terms Raise Dispute Over Website Crawling and AI Training Rights