Google announced the open-sourcing of TorchTPU at PyTorch Conference 2026, a project jointly developed with Meta's PyTorch team that makes TPU a native hardware option within the PyTorch ecosystem alongside GPU and CPU. The code is now available on GitHub, allowing existing PyTorch workloads to migrate to TPU with minimal modifications. Major frameworks including vLLM, SGLang, TorchTitan, VeRL, and Ray have already integrated TorchTPU support. In Hugging Face Transformers v5, developers can deploy models to TPU simply by passing `device_map="tpu"`. Benchmark tests showed TorchTitan training Qwen 3 8B achieved a maximum Model FLOPs Utilization (MFU) of 52%, scaling up to 8,192 Ironwood TPUs. Alongside TorchTPU, Google released llm-d 1.0 in collaboration with Red Hat and IBM, while LiteRT also joined the PyTorch ecosystem, further expanding alternatives to NVIDIA's dominant AI infrastructure stack.