Abstract Large Language Models (LLMs) have transformed natural language processing
tasks successfully. Yet, their large size and high computational needs pose challenges for …

The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a
unique blend of opportunities and challenges. Although the field has expanded and is …

Large language models (LLMs) have achieved remarkable advancements in natural
language processing, showcasing exceptional performance across various tasks. However …

Large Language Models (LLMs) have attracted extensive attention due to their remarkable
performance across various tasks. However, the substantial computational and memory …

Large Language Models (LLMs) have revolutionized the field of natural language
processing, achieving unprecedented performance across a variety of applications …

The potential use of large language models (LLMs) in healthcare robotics can help address
the significant demand put on healthcare systems around the world with respect to an aging …

Transformers have emerged as the backbone of large language models (LLMs). However,
generation remains inefficient due to the need to store in memory a cache of key-value …

The efficiency of large language models (LLMs) remains a critical challenge, particularly in
contexts where computational resources are limited. Traditional attention mechanisms in …

Recently, there has been an extensive research effort in building efficient large language
model (LLM) inference serving systems. These efforts not only include innovations in the …

Large language models (LLMs) demonstrate exceptional performance but incur high serving
costs due to substantial memory demands, with the key-value (KV) cache being a primary …