Scaling the amount of compute used to train language models has dramatically improved
their capabilities. However, when it comes to inference, we often limit the amount of compute …

Large language models (LLMs) are increasingly used for complex tasks that require multiple
generation calls, advanced prompting techniques, control flow, and structured …

Large language models (LLMs) are increasingly used for complex tasks that require multiple
generation calls, advanced prompting techniques, control flow, and structured …

The widespread of Large Language Models (LLMs) marks a significant milestone in
generative AI. Nevertheless, the increasing context length and batch size in offline LLM …

Large Language Models (LLMs) have revolutionized a wide range of applications with their
strong human-like understanding and creativity. Due to the continuously growing model size …

With the widespread deployment of long-context large language models (LLMs), there has
been a growing demand for efficient support of high-throughput inference. However, as the …

Large language models (LLMs) increasingly play an important role in a wide range of
information processing and management tasks. Many of these tasks are performed in large …

Analytical database providers (eg, Redshift, Databricks, BigQuery) have rapidly added
support for invoking Large Language Models (LLMs) through native user-defined functions …

Recent advancements in Large Language Models (LLMs) have significantly increased
context window sizes, enabling sophisticated applications but also introducing substantial …

Long-context LLMs have enabled numerous downstream applications but also introduced
significant challenges related to computational and memory efficiency. To address these …