Faulty Nvidia H100 GPUs and HBM3 Memory: Major Cause of Failures During Llama 3 Training.
Matilda
Faulty Nvidia H100 GPUs and HBM3 Memory: Major Cause of Failures During Llama 3 Training.
Meta's ambitious Llama 3 project represents a groundbreaking effort in the field of artificial intelligence. Utilizing a massive cluster of 16,384 Nvidia H100 80GB GPUs, the scale and complexity of the training operation are immense. Over a span of 54 days, the system experienced 4 ft19 unexpected component failures, averaging one failure every three hours. Half of these failures were attributed to faulty Nvidia H100 GPUs or their HBM3 memory. This article delves into the intricacies of these failures, their impact on the training process, and the strategies employed to mitigate such issues. The Llama 3 Project: An Overview Llama 3, developed by Meta, is one of the most advanced AI models in existence. Designed to push the boundaries of artificial intelligence, it requires an extraordinary amount of computational power. The decision to deploy 16,384 Nvidia H100 GPUs underscores the project's scale and the complexity involved in training such a sophisticated model. Importance of R…
