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

This work studies post-training parameter quantization in large language models (LLMs).
We introduce quantization with incoherence processing (QuIP), a new method based on the …

Large language models (LLMs) have exploded a new heatwave of AI for their ability to
engage end-users in human-level conversations with detailed and articulate answers across …

Large language models (LLMs) face the challenges in fine-tuning and deployment due to
their high memory demands and computational costs. While parameter-efficient fine-tuning …

Large language models (LLMs) have been applied in various applications due to their
astonishing capabilities. With advancements in technologies such as chain-of-thought (CoT) …

This paper introduces SpecInfer, a system that accelerates generative large language model
(LLM) serving with tree-based speculative inference and verification. The key idea behind …

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

Many networking tasks now employ deep learning (DL) to solve complex prediction and
optimization problems. However, current design philosophy of DL-based algorithms entails …

Abstract Large Language Models (LLMs) have shown promising performance in knowledge-
intensive reasoning tasks that require a compound understanding of knowledge. However …

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 …