Original post · AI translation
@sundarpichai 真酷 https://t.co/DL1SxSnOpg
在一年内推出同一代的 4 种不同 TPU 系统！硬件工程师与 Google DeepMind 团队共同设计系统，无需等待传统半导体周期所需的数年时间，这意味着我们可以持续快速地为客户创新。
Original
Four distinct TPU systems in a single generation in one year! Instead of waiting years for a traditional semiconductor cycle, hardware engineers co-designing systems alongside Google Deepmind means we can keep innovating quickly for customers.
很高兴分享 @Google 的 AI 与基础设施团队取得的一个重大里程碑。
今天，我们正在扩充第 8 代 TPU 产品组合，新增：
• TPU 8i Max：专为超长上下文窗口和每机架最高计算密度而打造
• TPU 8tx：专为安全、保密的推理而设计
这是我们首次在一年内为同一代产品推出 4 种不同的 TPU 系统。
对于开发者来说，硬件的价值取决于软件栈。我们希望你们能像我们在 Google 一样，轻松高效地使用 TPU 进行开发。现在，你们可以：
↳ 通过 TorchTPU 运行原生 PyTorch 和 Hugging Face 模型（无需重写代码）
↳ 借助开源 TPU Sync 将 KV 传输延迟降低超过 90%
↳ 借助 Agent Compute Engine 消除计算资源闲置造成的浪费
在我们的博客上了解更多有关最新 TPU 的信息：https://t.co/8eaFhq3jhi
我期待与不断拓展 AI 和计算领域边界的开发者、研究人员、工程师和机构交流！
Original
Thrilled to share a big milestone from our AI & Infrastructure team at @Google. Today we’re expanding our 8th generation TPU portfolio with: • TPU 8i Max: Built for massive context windows and the highest compute density per rack • TPU 8tx: Purpose-built for secure, confidential inference This marks the first time we’ve introduced four distinct TPU systems in a single generation, in one year. For developers, hardware is only as good as the software stack. And we want you to be able to build with TPUs as easily and efficiently as we do at Google. You can now: ↳ Run native PyTorch & Hugging Face models via TorchTPU (zero code rewrites) ↳ Cut KV transfer latency >90% with open-source TPU Sync ↳ Eliminate idle compute waste with Agent Compute Engine Read more about our newest TPUs on our blog: https://t.co/8eaFhq3jhi I look forward to connecting with builders, researchers, engineers and organizations pushing the boundaries of AI and computing!