The advent of large language models (LLMs) revolutionized natural language processing
applications, and running LLMs on edge devices has become increasingly attractive for …

The rapid adoption of large language models (LLMs) has led to significant advances in
natural language processing and text generation. However, the energy consumed through …

Large language model (LLM) serving is becoming an increasingly critical workload for cloud
providers. Existing LLM serving systems focus on interactive requests, such as chatbots and …

Despite that prior work of batched inference and parameter-efficient fine-tuning techniques
have reduced the resource requirements of large language models (LLMs), challenges …

The widely adopted and powerful generative large language models (LLMs) have raised
concerns about intellectual property rights violations and the spread of machine-generated …

In machine learning (ML), the inference phase is the process of applying pre-trained models
to new, unseen data with the objective of making predictions. During the inference phase …

In Large Language Model (LLM) inference, the output length of an LLM request is typically
regarded as not known a priori. Consequently, most LLM serving systems employ a simple …

The rise of Foundation Models (FMs) like Large Language Models (LLMs) is revolutionizing
software development. Despite the impressive prototypes, transforming FMware into …

Large Language Models (LLMs) are increasingly used in various applications but are
computationally complex and energy-consuming due to the high volume of off-chip memory …

Both the training and use of Large Language Models (LLMs) require large amounts of
energy. Their increasing popularity, therefore, raises critical concerns regarding the energy …