The post says TorchTPU exposes a native torch.device("tpu") backend in PyTorch, while the fork adds TPU-oriented training recipes and execution paths.
The account highlights SimpleFSDP, which expresses parameter sharding through DTensor parametrization so collectives appear in the traced graph, and Graph Trainer, which captures a full training step. Google reports 29%+ Model Flops Utilization on Qwen3 235B for this path. That figure is a vendor-reported result tied to the described workload and setup, not evidence of general performance parity across models or deployments.