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

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 …

In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …

The advent of Large Language Models (LLMs) represents a notable breakthrough in Natural
Language Processing (NLP), contributing to substantial progress in both text …

Large Language Models (LLMs) with billions of parameters have drastically transformed AI
applications. However, their demanding computation during inference has raised significant …

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

Large language models (LLMs) have revolutionized the field of AI, demonstrating
unprecedented capacity across various tasks. However, the inference process for LLMs …

Six-bit quantization (FP6) can effectively reduce the size of large language models (LLMs)
and preserve the model quality consistently across varied applications. However, existing …

Generative tasks, such as text generation and question answering, hold a crucial position in
the realm of mobile applications. Due to their sensitivity to privacy concerns, there is a …