Large foundation models, including large language models, vision transformers, diffusion,
and large language model based multimodal models, are revolutionizing the entire machine …

In public clouds, users must manually select a datacenter region to upload their ML training
data and launch ML training workloads in the same region to ensure data and computation …

Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …

Training large AI models on numerous GPUs consumes a massive amount of energy,
making power delivery one of the largest limiting factors in building and operating …

Large Language Models (LLMs) like GPT and LLaMA are revolutionizing the AI industry with
their sophisticated capabilities. Training these models requires vast GPU clusters and …

With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …

Deep learning (DL) jobs use multi-dimensional parallelism, ie, combining data, model, and
pipeline parallelism, to use large GPU clusters efficiently. Long-running jobs may …

Training large Deep Neural Network (DNN) models requires thousands of GPUs over the
course of several days or weeks. At this scale, failures are frequent and can have a big …

Multimodal large language models (LLMs) have demonstrated significant potential in a wide
range of AI applications. Yet, training multimodal LLMs suffers from low efficiency and …

Reinforcement Learning from Human Feedback (RLHF) is widely used in Large Language
Model (LLM) alignment. Traditional RL can be modeled as a dataflow, where each node …