5 citations · 8 across the 10 of their papers we have counts for
5 papers · 1 filter
Accelerating Large Language Model Training with Hybrid GPU-based Compression
Lang Xu, Quentin Anthony, Qinghua Zhou +5
Data Parallelism (DP), Tensor Parallelism (TP), and Pipeline Parallelism (PP) are the three strategies widely adopted to enable fast and efficient Large Language Model (LLM) traini…
Demystifying the Communication Characteristics for Distributed Transformer Models
Quentin Anthony, Benjamin Michalowicz, Jacob Hatef +5
Deep learning (DL) models based on the transformer architecture have revolutionized many DL applications such as large language models (LLMs), vision transformers, audio generation…
Comparative Study of Large Language Model Architectures on Frontier
Junqi Yin, Avishek Bose, Guojing Cong +2
Large language models (LLMs) have garnered significant attention in both the AI community and beyond. Among these, the Generative Pre-trained Transformer (GPT) has emerged as the d…
The Case for Co-Designing Model Architectures with Hardware
Quentin Anthony, Jacob Hatef, Deepak Narayanan +6
While GPUs are responsible for training the vast majority of state-of-the-art deep learning models, the implications of their architecture are often overlooked when designing new d…
Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference
Jinghan Yao, Quentin Anthony, Aamir Shafi +3
In large language models like the Generative Pre-trained Transformer, the Mixture of Experts paradigm has emerged as a powerful technique for enhancing model expressiveness and acc…