JAX is an ML framework that has been widely adopted by foundation model builders because of its advantages like high performance, scalability, composability, and ease of programmability. In this session, we will showcase the entire ecosystem supported by JAX for end-to-end foundation model building from data loading to training and inference on both TPUs and GPUs. We'll highlight the entire JAX stack, including high performance implementations for large language models and diffusion models in MaxText and MaxDiffusion. Learn how customers such as Assembly AI, Cohere, Anthropic, MidJourney, Stability AI, and partners like Nvidia, have adopted JAX for building foundation models on Google Cloud and beyond.

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Implementing generative AI applications with large language models (LLMs), and diffusion models requires large amounts of computation that can seamlessly scale to train, fine-tune, and serve the models. Google Cloud TPUs. Cohere is leveraging the compute-heavy Cloud TPU v4 and v5e to train sophisticated gen AI models that meet the heightened needs of their enterprise users. Check out how Cohere and Cloud TPUs are delivering enterprise-tailored large language models (LLMs) that can help increase business productivity by automating time-consuming and monotonous workflows. Please note: seating is limited and on a first-come, first served basis; standing areas are available

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.