Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

A new study by researchers at the University of Toronto suggests that one of the fundamental assumptions of deep learning artificial intelligence models – that they require enormous amounts of ...

This is a schematic showing data parallelism vs. model parallelism, as they relate to neural network training. Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news ...

If they didn’t, you wouldn’t have a single training run, you’d have 200,000 chips training 200,000 models on their own. That data-sharing process starts with “checkpointing”, in which a ...

Today, LLMs leverage distributed training across thousands of GPUs or specialized hardware such as tensor processing units (TPUs), combined with optimized software frameworks. Innovations in cloud ...

“This allows users to leverage SageMaker’s distributed training capabilities, such as data parallelism and model parallelism, across multiple compute instances, enabling scalable, efficient ...

Parallel Domain, a startup developing a platform for synthesizing AI model training data, has raised $11 million. Skip to main content Events Video Special Issues Jobs ...

A technical paper titled “Optimizing Distributed Training on Frontier for Large Language Models” was published by researchers at Oak Ridge National Laboratory (ORNL) and Universite Paris-Saclay.