High throughput serving of large language models (LLMs) requires batching sufficiently
many requests at a time. However, existing systems struggle because the key-value cache …

Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …

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 Language Models (LLMs) have demonstrated remarkable capabilities in important
tasks such as natural language understanding and language generation, and thus have the …

Emotional intelligence significantly impacts our daily behaviors and interactions. Although
Large Language Models (LLMs) are increasingly viewed as a stride toward artificial general …

Diffusion models have achieved great success in synthesizing high-quality images.
However generating high-resolution images with diffusion models is still challenging due to …

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 high computational and memory requirements of generative large language models
(LLMs) make it challenging to serve them cheaply. This paper aims to reduce the monetary …

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 …

The increasing demand for improving deep learning model performance has led to a
paradigm shift in supporting low-precision computation to harness the robustness of deep …