Discover the cutting edge of foundation model development with JAX on Google Cloud. This session will showcase the latest advancements in the JAX ecosystem, including optimized performance on TPUs and GPUs. Explore new, high-performance models powered by MaxText and MaxDiffusion, delve into enhanced JAX libraries and Stable Stack packages, and learn about advanced diagnostics tools. Gain insights into how leading customers and partners are leveraging JAX on Google Cloud to build and deploy next-generation foundation models at scale.

Maximize the efficiency of your machine learning workloads on Google Cloud infrastructure. This session delivers actionable strategies for in-depth diagnostics on both TPUs and GPUs. Learn to visualize model architectures, analyze critical performance metrics, and pinpoint optimization opportunities. Empower your data-driven decision-making and achieve significant performance gains through practical diagnostic techniques.

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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