Efficiently training large language models (LLMs) necessitates the adoption of hybrid
parallel methods, integrating multiple communications collectives within distributed …

As large language models (LLMs) become widespread in various application domains, a
critical challenge the AI community is facing is how to train these large AI models in a cost …

Large language models (LLMs) with long sequences begin to power more and more
fundamentally new applications we use every day. Existing methods for long-sequence LLM …

Large language models (LLMs) have demonstrated remarkable success as foundational
models, benefiting various downstream applications through fine-tuning. Loss scaling …

Efficiently training large language models requires parallelizing across hundreds of
hardware accelerators and invoking various compute and memory optimizations. When …

Training extremely large language models with billions of parameters is a computationally
intensive task that pushes the limits of current data parallel training systems. While …

The emergence of large language models (LLMs) relies heavily on distributed training
strategies, among which pipeline parallelism plays a crucial role. As LLMs' training …

The fundamental success of large language models hinges upon the efficacious
implementation of large-scale distributed training techniques. Nevertheless, building a vast …

The emergence of Large Language Models (LLMs) has necessitated the adoption of parallel
training techniques, involving the deployment of thousands of GPUs to train a single model …

In training of modern large natural language processing (NLP) models, it has become a
common practice to split models using 3D parallelism to multiple GPUs. Such technique …