We make a case for any-precision LLM, which enables memory-eficient and cost-effective deployment of multiple, different-sized LLMs. We propose a lightweight method for any …

In this work, we introduced AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, …

We introduce AnyGPT, an any-to-any multi- modal language model that utilizes discrete representations for the unied processing of various modalities, including speech, text, images, …

In pursuit of this goal, we present NExT-GPT, an any-to-any MM-LLM designed to seamlessly handle input and output in any combination of four modalities: text, image, video, and audio.

Introduction Large language models are becoming increasingly capable of handling complex, multi-step tasks. Advances in reasoning, multimodality, and tool use have unlocked a new …

We demonstrate that our LLM-centric network architecture archives the same performance as a full-bisection bandwidth any-to-any Clos cluster while reducing the cost by 37% to 75%.

We estimate 66% of US adults either not using any LLM or not using them for work/study purposes, with a further 10% reporting less than an hour of usage per week.