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The LLM360 project advances AI through open-source foundation models and datasets. This talk explores key initiatives including K2, the most capable fully open-source language model, and TxT360, examining the true meaning of open source while proposing new approaches to academic and industry collaboration in open-source AI.

In this talk, we will discuss the lessons learned in building an efficient large language model deployment system for both server and edge settings. We will cover general techniques in machine learning compilation and system support for efficient structure generation. We will also discuss the future opportunities in system co-design for cloud-edge model deployments.

Numerous domain-specific accelerators have been developed recently to address the growing computational needs of machine learning, and the success of these DSAs hinges on effective ML compilers like Google's XLA, which enhances ML performance on various hardware and supports multiple frameworks, and is further advanced through collaborative development in OpenXLA.