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  1. Google Developers Blog60

    Google 将 TorchTitan 接入 TorchTPU，并发布 TPU 模型优化手册

    Google released a TPU fork of Meta's TorchTitan that runs PyTorch training on Cloud TPUs through TorchTPU's native torch.device("tpu") backend, with TPU-native kernels and a step-by-step optimization playbook. On TPU v7x (Ironwood), Llama 3 405B holds about 57% MFU from 128 to 1,024 chips, Qwen3 235B reaches 32.42% MFU on one 1,024-chip slice, and FLUX.1 reaches 19.4% MFU on 1,024 chips at 93% weak-scaling efficiency. The fork is at https://github.com/google-pytorch/torchtitan/, and a Transformers modeling backend plus an agent onboarding skill extend the same trainer to other checkpoints.

    Why it matters: Google 与 Meta 合作把 TorchTitan 移植到 TPU，并给出从 FLUX.1 到 Qwen3 235B 的逐级调优路径与 MFU 数字，可作为 PyTorch 团队评估 TPU 训练可行性的参考。

  1. Cursor Changelog62

    Cursor analyzes Llama-2-70B inference cost and latency against GPT-3.5

    Cursor measured Llama-2-70B inference on 2 80-GB A100 GPUs and found it over 3x cheaper than GPT-3.5 for prompt tokens but far more expensive for generated tokens at small batch sizes. Prompt processing costs about $0.00042 per 1K tokens versus GPT-3.5's $0.0015, while generation reaches $0.066 per 1K tokens at batch size 1 and only approaches GPT-3.5 pricing at large batches that push time to first token to several seconds. Cursor recommends open-source models for prompt-heavy tasks like classification and reranking, and closed-source APIs for generation-heavy workloads.

    Why it matters: Cursor's measured cost and latency data shows when self-hosting Llama-2-70B beats GPT-3.5 and when it does not, offering a reusable framework for open-source model serving decisions.

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