Accelerate foundation model training and inference with Amazon SageMaker HyperPod and Amazon SageMaker Studio

Modern generative AI model providers require unprecedented computational scale, with pre-training often involving thousands of accelerators running continuously for days, and sometimes months. Foundation Models (FMs) demand distributed training clusters — coordinated groups of accelerated compute instances, using frameworks like PyTorch — to parallelize workloads across hundreds of accelerators (like AWS Trainium and AWS Inferentia chips or NVIDIA GPUs).

Orchestrators like SLURM and Kubernetes manage these complex workloads, scheduling jobs across nodes, managing cluster resources, and processing requests. Paired with AWS infrastructure like Amazon Elastic Compute Cloud (Amazon EC2) accelerated computing instances, Elastic Fabric Adapter (EFA), and distributed file systems like Amazon Elastic File System (Amazon EFS) and Amazon FSx, these ultra clusters can run large-scale machine learning (ML) training and inference, handling parallelism, gradient synchronization and collective communications, and even routing and load balancing. However, at scale, even robust orchestrators face challenges around cluster resilience. Distributed training workloads specifically run synchronously ...