We made these chips programmable via TensorFlow, allowing models to run on CPUs, GPUs, or TPUs with minimal modifications.

To optimize AI applications, we need to predict multiple tokens at once, align hardware selection with application needs, and implement various tuning mechanisms for quality.

Google is committed to both TPUs and GPUs, leveraging both for training and serving AI models, while maintaining flexibility in their infrastructure.

We faced unique challenges in scaling our platform, particularly with GPU local attention caching, which led to unexpected negative results during testing.

While a 10 million GPU synchronous pre-training run may not look like current methods, there will likely be some form of training run at that scale in the future, and I would call the learning process semi-synchronous, as it involves many GPUs working together on an AI system, but not all parts will necessarily communicate with each other.

Companies are increasingly using multiple AI models for different tasks, optimizing performance by selecting the best model for each specific application.

We conduct long-term research independent of any particular application and build open-source systems to assist with our research and deployment of machine learning models, like TensorFlow.

Our reinforcement learning approach can achieve placements that are 20% faster than those determined by human experts.

The effort to parallelize work aggressively during the run was crucial to avoid performance cliffs that could make the model untrainable.