From silicon to softmax: Inside the Ironwood AI stack

As machine learning models continue to scale, a specialized, co-designed hardware and software stack is no longer optional, it’s critical. Ironwood, our latest generation Tensor Processing Unit (TPU), is the cutting-edge hardware behind advanced models like Gemini and Nano Banana, from massive-scale training to high-throughput, low-latency inference. This blog details the core components of Google's AI software stack that are woven into Ironwood, demonstrating how this deep co-design unlocks performance, efficiency, and scale. We cover the JAX and PyTorch ecosystems, the XLA compiler, and the high-level frameworks that make this power accessible.

1. The co-designed foundation

Foundation models today have trillions of parameters that require computation at ultra-large scale. We designed the Ironwood stack from the silicon up to meet this challenge.

The core philosophy behind the Ironwood stack is system-level co-design, treating the entire TPU pod not as a collection of discrete accelerators, but as a single ...