Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …

Large language models (LLMs) have shown remarkable capabilities in language
understanding and generation. However, such impressive capability typically comes with a …

We show for the first time that large-scale generative pretrained transformer (GPT) family
models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal …

As their size increases, Large Languages Models (LLMs) are natural candidates for network
pruning methods: approaches that drop a subset of network weights while striving to …

Transformer-based large language models (LLMs) demonstrate impressive performance
across various natural language processing tasks. Serving LLM inference for generating …

Generative Large Language Models (LLMs) have demonstrated remarkable results for a
wide range of tasks. However, deploying these models for inference has been a significant …

The recent emergence of Large Language Models based on the Transformer architecture
has enabled dramatic advancements in the field of Natural Language Processing. However …

Recent advances in state-of-the-art DNN architecture design have been moving toward
Transformer models. These models achieve superior accuracy across a wide range of …

Structured pruning of modern large language models (LLMs) has emerged as a way of
decreasing their high computational needs. Width pruning reduces the size of projection …