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 …

The burgeoning field of Large Language Models (LLMs), exemplified by sophisticated
models like OpenAI's ChatGPT, represents a significant advancement in artificial …

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 are ubiquitous in natural language processing because they can
adapt to new tasks without retraining. However, their sheer scale and complexity present …

Large Language Models (LLMs) represent a class of deep learning models adept at
understanding natural language and generating coherent text in response to prompts or …

Transformer based large language models have achieved tremendous success. However,
the significant memory and computational costs incurred during the inference process make …

Large Language Models (LLMs) stand out for their impressive performance in intricate
language modeling tasks. However, their demanding computational and memory needs …

The Large Language Models (LLMs) have been popular and widely used in creative ways
because of their powerful capabilities. However, the substantial model size and complexity …

Quantization has emerged as a promising technique for improving the memory and
computational efficiency of large language models (LLMs). Though the trade-off between …

This paper focuses on addressing the substantial memory footprints and bandwidth costs
associated with the deployment of Large Language Models (LLMs). LLMs, characterized by …