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 …

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 …

Model parallelism is conventionally viewed as a method to scale a single large deep
learning model beyond the memory limits of a single device. In this paper, we demonstrate …

Transformer-based large language models (LLMs) demonstrate impressive performance
across various natural language processing tasks. Serving LLM inference for generating …

Inference serving for large language models (LLMs) is the key to unleashing their potential
in people's daily lives. However, efficient LLM serving remains challenging today because …

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 …

This paper presents ServerlessLLM, a distributed system designed to support low-latency
serverless inference for Large Language Models (LLMs). By harnessing the substantial near …

Large language models (LLMs) power a new generation of interactive AI applications
exemplified by ChatGPT. The interactive nature of these applications demands low latency …

Large Language Models (LLMs) have presented impressive performance across several
transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster …

Low-rank adaptation (LoRA) is a popular approach to finetune pre-trained large language
models (LLMs) to specific domains. This paper introduces dLoRA, an inference serving …