Google与Meta开源PyTorch TPU后端TorchTPU
Google与Meta的PyTorch团队将TorchTPU作为开源项目发布，为PyTorch工作负载提供原生运行Google Cloud TPU的后端。该项目支持标准PyTorch API、即时模式调试和torch.compile优化，并覆盖Hugging Face Transformers、Diffusers、vLLM、SGLang、llm-d、TorchTitan及LiteRT等工具。Google称，其训练方案已在最多8000颗Ironwood TPU上完成验证，Qwen3-8B测试最高达到52%的模型浮点运算利用率；这些数据均为厂商声称。
TorchTPU为开发者提供了一条将PyTorch工作负载迁移至Google TPU基础设施的原生路径，同时可继续使用熟悉的训练、推理和部署工具，也扩展了Google历史上主要围绕TensorFlow和JAX发展的AI软件生态。
If you ever tried to run PyTorch on a TPU, you probably ported your code to torch_xla, or you ended up having to learn JAX. With TorchTPU, a native PyTorch backend, TPU is now just another device. It opens up native support of TPU in the Hugging Face ecosystem. Transformers, Diffusers and Kernels run natively on TPU. Pick a model from the Hub, send it to "tpu", and you're done.
Wait, what's a TPU? 🤔
TPU stands for Tensor Processing Unit. TPUs are Google's own AI accelerators. They are chips built for one job: the matrix math that neural networks spend most of their time on. Google has been designing them for about ten years, and uses them to train and serve its own models, including Gemini.
TPUs are available on Google Cloud, from a single chip up to pods of thousands of chips. They shine when you need a lot of compute, for training or for serving at scale.
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