Foundation models continue to grow in size with billions or even trillions of parameters, and they often won’t fit into a single accelerator device such as a GPU. Amazon SageMaker distributed training capabilities help you apply advanced parallelization techniques, communication optimizations, and efficient checkpointing strategies to distribute your training workload across hundreds or thousands of GPUs, reducing model training time and cost by up to 20%. Join this session for a deep dive into the infrastructure used to run distributed training at scale. Learn how to integrate Amazon SageMaker training capabilities to reduce the total cost of foundation model development.

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