Instead of tee() with its hidden unbounded buffer, you get explicit multi-consumer primitives. Stream.share() is pull-based: consumers pull from a shared source, and you configure the buffer limits and backpressure policy upfront.
"Computing demand is growing exponentially," boss Jensen Huang said. "Our customers are racing to invest in AI compute - the factories powering the AI industrial revolution and their future growth."
。谷歌浏览器【最新下载地址】对此有专业解读
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而拿下 Meta 这个全球最贪婪的算力吞噬兽，无疑是谷歌向英伟达下达的最强战书。同时，谷歌在底层软件生态上的妥协也立了大功——TPU 近期大幅优化了对 PyTorch（Meta 主导的 AI 框架）的原生支持，这让 Meta 的研发团队终于可以顺滑地将模型迁移到谷歌的硬件上。
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