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 …

Transformer-based large language model (LLM) inference serving is now the backbone of
many cloud services. LLM inference consists of a prefill phase and a decode phase …

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) are typically trained in two phases: pre-training on large
internet-scale datasets, and fine-tuning for downstream tasks. Given the higher …

Large language model (LLM) serving has transformed from stateless to stateful systems,
utilizing techniques like context caching and disaggregated inference. These optimizations …

Existing methods for adapting large language models (LLMs) to new tasks are not suited to
multi-task adaptation because they modify all the model weights--causing destructive …

Serving disaggregated large language models (LLMs) over tens of thousands of xPU
devices (GPUs or NPUs) with reliable performance faces multiple challenges. 1) Ignoring …

Navigating the intricacies of data management in the era of Large Language Models (LLMs)
presents both challenges and opportunities for database and data management …