This session focuses on distributed training of language models across multiple AWS Trainium instances using SageMaker HyperPod. We begin with an overview of the NxDT framework in multi-node environments and discuss how HyperPod enables efficient scaling of large model workloads. The session highlights best practices for coordination, communication efficiency, and kernel utilization across nodes. A demonstration illustrates distributed supervised fine-tuning of Llama 3 (8B) on HyperPod infrastructure, showcasing Trainium’s ability to scale training for advanced AI workloads. Subscribe to AWS: Sign up for AWS: AWS free tier: Explore more: Contact AWS: Next steps: Explore on AWS in Analyst Research: Discover, deploy, and manage software that runs on AWS: Join the AWS Partner Network: Learn more on how Amazon builds and operates software: Do you have technical AWS questions? Ask the community of experts on AWS re:Post: Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—use AWS to be more agile, lower costs, and innovate faster. #AWS #AI #GenerativeAI #AmazonWebServices #CloudComputing