Google 一年内推出四款 TPU 8代系统，新增 TPU 8i Max 与 8tx

配套软件栈方面，开发者可通过 TorchTPU 直接运行原生 PyTorch 与 Hugging Face 模型，无需改写代码；开源的 TPU Sync 可将 KV 传输延迟降低超过 90%；Agent Compute Engine 则用于消除空闲算力浪费。

Google 强调硬件工程师与 Google DeepMind 共同进行系统协同设计，缩短了传统半导体数年的迭代周期。对依赖推理成本与数据安全的企业客户而言，机密推理专用硬件与更低迁移门槛的软件栈，是这次扩展中最直接可用的部分。

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. Amin Vahdat @AminVahdat 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: bit.ly/3VfETPR I look forward to connecting with builders, researchers, engineers and organizations pushing the boundaries of AI and computing! 🔗 View Quoted Tweet📊 13 ⚡

This report is categorized under AITOP's "Products" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include TPU, Google, TorchTPU, PyTorch.

Source attribution: curated from Sundar Pichai (@sundarpichai).