Generative large language model (LLM) applications are growing rapidly, leading to large-
scale deployments of expensive and power-hungry GPUs. Our characterization of LLM …

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

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 …

Interacting with humans through multi-turn conversations is a fundamental feature of large
language models (LLMs). However, existing LLM serving engines executing multi-turn …

The context window of large language models (LLMs) is rapidly increasing, leading to a
huge variance in resource usage between different requests as well as between different …

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 rapid growth of Large Language Models (LLMs) has been a driving force in
transforming various domains, reshaping the artificial general intelligence landscape …

The rapid proliferation of Large Language Models (LLMs) has been a driving force in the
growth of cloud-based LLM services, which are now integral to advancing AI applications …

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

Abstract The evolution of Neural Machine Translation (NMT) has been significantly
influenced by six core challenges (Koehn and Knowles,) that have acted as benchmarks for …